Handbook of Data Science Approaches for Biomedical Engineering [1 ed.] 0128183187, 9780128183182

Table of contents :
Cover
Handbook of Data Science Approaches for Biomedical Engineering
Copyright
Contents
Contributors
1. Analysis of the role and scope of big data analytics with IoT in health care domain
1. Introduction
2. Sources of health care data
2.1 Electronic health records (EHR)
2.2 Clinical text mining
2.3 Medical imaging data
2.4 Genomic data
2.5 Behavioral data
3. Tools and data analytics interfaces in medical and health care system
3.1 Advanced data visualization (ADV)
3.2 Presto
3.3 Hive
3.4 Vertica
3.5 Key performance indicators (KPI)
3.6 Online analytics processing (OLAP)
3.7 Online transaction processing (OLTP)
3.8 The Hadoop distributed file system (HDFS)
3.9 Casandra file system (CFS)
3.10 Map reduce system
3.11 Complex event processing (CEP)
3.12 Text mining
3.13 Cloud computing
3.14 Mahout
3.15 JAQL
3.16 AVRO
4. Health care with big data challenges
4.1 Issues related to policy and fiscal factors
4.2 Issues related to technology
5. IoT defined
6. IoT for health care
7. Challenges for IoT in health care
8. Evolution of big data in medical IoT
9. Advantages
10. Literature survey
11. Implementation of a real-time big data analytics of IoT-based health care monitoring system
11.1 Components and methods
11.2 Results and discussion
12. Conclusion
References
2. Automated human cortical bone Haversian canal histomorphometric comparison system
1. Introduction
2. Sample collection
3. Sample preparation
3.1 Specimen defatting
3.2 Sectioning of bone specimen
3.3 Specimen grinding and polishing
3.4 Glass slide mounting
4. Difficulties in sample preparation
4.1 Trapped air bubbles in the glass sample
4.2 Thick bone slice
4.3 Uneven thickness
4.4 Broken section
4.5 Dirty specimen
4.6 Fragile bone specimen
5. Image acquisition
6. Microstructural parameter selection
6.1 Haversian canal number (hcn)
6.2 Mean Haversian canal area (hcm)
6.3 Total Haversian canal area
6.4 Mean Haversian canal radius
6.5 Mean Haversian canal perimeter
6.6 Percentage area covered by Haversian canal (hcpar)
7. Inclusion and exclusion criteria
8. Statistical tests
9. Automated comparison system
9.1 Comparison test selection
10. Automated system design
11. Sex comparison without age groups
11.1 Sex comparison hcm
11.2 Sex comparison hca
11.3 Sex comparison hcr
11.4 Sex comparison hcp
11.5 Sex comparison hcn
11.6 Sex comparison hcpar
11.7 Sex comparison discussion
12. Race comparison without age groups
12.1 Race comparison hcm
12.2 Race comparison hca
12.3 Race comparison hcr
12.4 Race comparison hcp
12.5 Race comparison hcn
12.6 Race comparison hcpar
12.7 Race comparison discussion
13. Conclusion
References
3. Biomedical instrument and automation: automatic instrumentation in biomedical engineering
1. Introduction
2. Biomedical instrumentation
3. Automation in the field of biomedical instrumentation
3.1 Automation in medical instruments
4. Automation in telerobotic surgeries
4.1 Origin of surgical robots
5. Types of robotic surgeries
5.1 Type-1 supervisory—controlled surgery systems
5.2 Type-2 shared-control robotic surgery systems
5.3 Type-3 tele surgical robotic surgery system
5.3.1 da Vinci Surgical System
5.3.2 ZEUS robotic surgical system (ZRSS)
5.3.3 Automated endoscopic system for optimal positioning (AESOP) robotic surgical system
6. Applications
6.1 PROS and CONS of surgical robots
6.1.1 PROS
6.1.2 CONS
6.2 The future of surgical robots
7. Automatic wireless sensor networking in biomedical instrumentation
8. Biomedical applications of wireless sensor networking
8.1 IEEE 802.15.4
8.2 Open system interconnect layered architecture
9. Network topology
10. Bluetooth communication
10.1 Bluetooth modules used for biomedical applications
11. Sensing technologies
11.1 Invasive biosensors for WSN
11.2 Noninvasive bio sensors for WSN
11.3 Respiration rate sensor
11.4 RF and antenna communication
12. Selecting RF transceivers
12.1 Specifications
12.2 Safety issues
13. Recent advancements and applications in biomedical instrumentation
13.1 Biomedical instrumentation in medical imaging
13.2 Biomedical instrumentation in medical devices
13.3 Biomedical instrumentation in tissue engineering
13.4 Biomedical instrumentation in implants and bionics
13.5 Biomedical instrumentation in clinical engineering
13.6 Biomedical instrumentation in neural engineering
13.7 Biomedical instrumentation in rehabilitation engineering
13.8 Applications of automation in biomedical instrumentation
14. Conclusion
References
4. Performance improvement in contemporary health care using IoT allied with big data
1. Introduction
1.1 Outline of IoT and big data
1.2 Technology modernization and quality as a challenge in health care systems
1.3 Availability of health care information in social media
1.4 Smart applications related to health care systems using IoT
1.5 ICT and big data in health care development
1.6 Cyber physical cloud computing and health care approaches
1.7 Big data analytic methods in health care
1.8 Decision making tools and logic implementation in big data
1.9 Quality assessment model in big data
1.10 Health care monitoring frameworks
2. Conclusion
References
5. Emerging trends in IoT and big data analytics for biomedical and health care technologies
1. Introduction
2. Big data workflow for biomedical image analysis
3. Role of artificial intelligence and robotics in telemedicine
3.1 Robotics in health care
3.2 History of robotics
3.3 Tele-surgery/remote surgery
3.4 Applications
3.5 Artificial intelligence (AI)
3.6 Internet of Robotic Things (IoRT)
4. Wearable devices and IoT
4.1 Classification and categories of wearable devices
4.2 Communication modes of wearable devices in IoT
4.3 Very short distance
4.4 Short distance
4.5 Long distance communication
4.6 Working principles of wearable devices in IoT
4.7 Applications of wearable devices in IoT
4.8 Research challenges and open issues
5. Biotechnological advances
5.1 Neuroscience and brain research
5.2 Gene therapy
5.3 Big data enhancing stem cell research and tissue engineering
5.4 Big data of nanotechnology to nanomedicine
5.5 New drug discovery and drug delivery systems
6. Conclusion
References
6. Recent advances on big data analysis for malaria prediction and various diagnosis methodologies
1. Introduction
2. Disease prediction model based on big data analysis
3. Diagnosis techniques
3.1 Clinical diagnosis
3.2 Manual microscopic examination of blood smear
3.3 Quantitative buffy coat (QBC)
3.4 Rapid diagnostic test (RDT)
3.5 Computerized diagnosis
3.5.1 Database collection setup
3.5.2 Preprocessing of blood smear image
3.5.3 Segmentation
3.5.3.1 Erythrocyte segmentation
3.5.3.2 Infected erythrocyte and parasite segmentation
3.5.4 Microscopic feature extraction
3.5.5 Feature selection
3.5.6 Malaria infection identification
3.5.6.1 k-Nearest neighbor
3.5.6.2 Neural network
3.5.6.3 Support vector machine
3.5.6.4 Naïve Bayes
3.5.6.5 Multivariate regression
3.5.6.6 Ada-Boost
3.5.6.7 Euclidean distance classifier
3.5.6.8 Hybrid classifier
3.5.7 Computer-aided malaria diagnosis
4. Discussion
5. Conclusion
Acknowledgments
References
Chapter 7 - Semantic interoperability in IoT and big data for health care: a collaborative approach
1. Introduction
2. State of the art
2.1 Internet of Things (IoT)
2.2 Cloud computing
2.3 U-health care system
2.3.1 Body Area Network (BAN)
2.3.1.1 Wireless Body Area Network (WBAN)
2.3.1.2 Personal monitoring devices (PMD)
2.3.2 Intelligent Medical Server (IMS)
2.3.3 Hospital system
3. Semantic interoperability
3.1 Ontologies and Standards
3.2 Mapping Technologies for Data Models
3.3 Data integration and exchange systems
3.4 Semantic annotations
4. Semantic interoperability in IoT health care
4.1 Adding semantic annotations to the IoT health care data
4.2 Experiments and results
5. SI in big data health care
5.1 Adding semantic annotations to the big data health care data
5.2 Experiments and results
6. Conclusion and future work
References
8. Why big data, and what it is: basics to advanced big data journey for the medical industry
1. Introduction
2. Why big data?
2.1 Application to medical industry
2.1.1 Big data in a medical domain
2.1.2 Electronic health records
2.1.3 Real-time alerts
2.1.4 Evidence-based medicine
2.1.5 Hospital readmissions
2.1.6 Fraud detection
3. Health care and the four Vs of big data
4. An architecture of large-scale platform to develop a predictive model
4.1 Types of big data
4.1.1 Primitive big data
4.1.2 Nonprimitive big data analytics
4.2 Platform to big data
4.2.1 The Hadoop Distributed File System (HDFS)
4.2.2 Map Reduce
4.2.3 PIG and PIGLatin
4.2.4 Jaql
4.2.5 HBase
4.2.6 Cassandra
4.2.7 Avro
4.2.8 Hive
5. The model through big data analytics
5.1 An architecture of large-scale platform to develop a predictive model
5.1.1 Map Reduce (Map and Reduce)
5.2 Functional network algorithm
5.2.1 Functional network (n/w) can be learned by the use of one of the optimization techniques
5.2.1.1 Model selection
6. Impact of big data
6.1 Examples to complex biomedical information
6.1.1 Dell health care solutions
6.1.2 IBM health care and life sciences
6.1.3 Intel health care
6.1.4 Amazon web services
6.1.5 GE health care life science
6.1.6 Oracle life sciences
6.1.7 Cisco health care solutions
6.2 Personalized medicines
7. Ethical issues
7.1 Ethical themes
7.1.1 Consent
7.1.1.1 Informed Consent
7.1.1.2 Single-instance consent
7.1.1.3 “Broad” and “Blanket” consent mechanisms
7.1.1.4 Tired consent
7.1.2 Data protection
7.1.3 Privacy
7.1.4 Ownership
7.1.5 Epistemology
7.1.6 Objectivity
8. Conclusion
References
Further reading
9. Semisupervised fuzzy clustering methods for X-ray image segmentation
1. Introduction
Part 1: Theory background
1.1 Image segmentation problem
1.2 Data clustering
1.3 Fuzzy clustering
1.4 Semisupervised fuzzy clustering
1.4.1 Semisupervised entropy regularized fuzzy clustering-eSFCM
Part 2: The combination of eSFCM and OTSU in image segmentation
2.1 The general diagram of the integration between the eSFCM and OTSU
2.2 OTSU threshold algorithm in image processing
Part 3: Semisupervised fuzzy clustering with spatial feature
3.1 The general framework
3.2 Determining suitable additional information
3.3 The semisupervised fuzzy clustering algorithm (SSFC-SC)
3.3.1 Dental image segmentation model
3.3.2 Solving the segmentation problem using Lagrange multiplier
3.4 Fuzzy satisficing method and semisupervised clustering method in segmentation problem (SSFC-FS )
3.5 The properties and consequences from solution analysis
3.6 The advantages of the proposed algorithms
Part 4: Defining the suitable additional information for SSFC-FS algorithm
4.1 The framework of the SSFC-FSAI method
4.2 The set of additional information functions
4.3 Defining an appropriate additional information
4.4 Advantages of the new algorithm
Part 5: The results of implementations and applications
5.1 Dental X-ray image dataset
5.1.1 Data description
5.1.2 Defining features
5.1.3 The validity indices and evaluation criteria
5.2 The performance among segmentation methods
5.2.1 Experiments in dental X-ray image dataset
5.2.2 The results of clustering algorithms according to changing of parameters
2. Conclusions
Acknowledgments
References
Index
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
T
U
V
W
X
Z
Back Cover

Citation preview

Handbook of Data Science Approaches for Biomedical Engineering Edited by Valentina Emilia Balas Vijender Kumar Solanki Raghvendra Kumar Manju Khari

Academic Press is an imprint of Elsevier 125 London Wall, London EC2Y 5AS, United Kingdom 525 B Street, Suite 1650, San Diego, CA 92101, United States 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom Copyright © 2020 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN: 978-0-12-818318-2 For information on all Academic Press publications visit our website at https://www.elsevier.com/books-and-journals

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Contents Contributors .................................................................................................... xiii Chapter 1: Analysis of the role and scope of big data analytics with loT in health care domain .................................................................. 1 Sushruta Mishra, Brojo Kishore Mishra, Hrudaya Kumar Tripathy and Arijit Dutta I.

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References

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Chapter 2: Automated human cortical bone Haversian canal histomorphometric comparison system Hadi Abdullah, Muhammad Mahadi Abdul Jamil, ljaz Khan,

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Contents 11.1 Sex comparison hcm ...................................................................................... 53 11.2 Sex comparison hca ........................................................................................ 54 11.3 Sex comparison hcr ........................................................................................ 54 11.4 Sex comparison hcp........................................................................................ 56 11.5 Sex comparison hcn........................................................................................ 56 11.6 Sex comparison hcpar .................................................................................... 57 11.7 Sex comparison discussion............................................................................. 57 12. Race comparison without age groups ................................................................ 58 12. I Race comparison hcm .................................................................................... 59 12.2 Race comparison hca ...................................................................................... 60 12.3 Race comparison hcr ...................................................................................... 61 12.4 Race comparison hcp ...................................................................................... 61 12.5 Race comparison hcn...................................................................................... 62 12.6 Race comparison hcpar .................................................................................. 62 13.

12.7 Race comparison discussion ........................................................................... 64 Conclusion........................................................................................................... 65

References

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Chapter 3: Biomedical instrument and automation: automatic instrumentation in biomedical engineering . .. . .. .. . .. . . .. .. . .. .. .. . .. . .. . .. .. 69 RJ. Hemalatha, R. Chandrasekaran, T.R. Thamizhvani, A. Josephin Arockia Dhivya, K. Sangeethapriya, A. Keerthana and G. Srividhya .

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2. Biomedical instrumentation ................................................................................ 72 3. Automation in the field of biomedical instrumentation .................................... 78 3.1 Automation in medical instruments ................................................................. 80 4. Automation in telerobotic surgeries ................................................................... 80 4.1 Origin of surgical robots .................................................................................. 81 5.

Types of robotic surgeries .................................................................................. 81 5.1 Type-I supervisory-controlled surgery systems ............................................ 81 5.2 Type-2 shared-control robotic surgery systems ............................................... 82

5.3 Type-3 tele surgical robotic surgery system .................................................... 82 6. Applications ........................................................................................................ 83 6.1 PROS and CONS of surgical robots ................................................................ 83 6.2 The future of surgical robots............................................................................ 84 7. Automatic wireless sensor networking in biomedical instrumentation ............ 84 8. Biomedical applications of wireless sensor networking ................................... 85 8.1 IEEE 802.15.4 ................................................................................................... 86 9.

8.2 Open system interconnect layered architecture ............................................... 86 Network topology ............................................................................................... 87

10. Bluetooth communication ................................................................................... 87 10.1 Bluetooth modules used for biomedical applications ................................... 88

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Contents I I. Sensing technologies

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Chapter 4: Performance improvement in contemporary health care using loT allied with big data Mamata Rath and Vijender Kumar Solanki

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Chapter 7: Semantic interoperability in 10 T and big data for health care: a collaborative approach Sivadi Balakrishna and M. Thirumaran

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Chapter 9: Semisupervised fuzzy clustering methods for X-ray image segmentation Tran Manh Tuan, Tran Thi Ngan, Do Nang Toan, Cu Nguyen Giap and Le Hoang Son

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Contributors Muhammad Mahadi Abdul Jamil Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia (UTHM), BatuPahat, Malaysia Hadi Abdullah Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia (UTHM), BatuPahat, Malaysia Sivadi Balakrishna Department of CSE, Pondicherry Engineering College, Pondicherry University, Puducherry, India Amit Banerjee Microelectronic Technologies & Devices, Department of Electrical and Computer Engineering, National University of Singapore, Singapore Debabrata Biswas NUS-HUJ-CREATE Molecular Mechanism of Inflammation and Disease, Department of Microbiology and Immunology, National University of Singapore, Singapore Chinmay Chakraborty Electronics and Communication Engineering, Birla Institute of Technology, Mesra, Jharkhand, India R. Chandrasekaran Department of Biomedical Engineering, Vels Institute of Science, Technology and Advanced studies, (Deemed to be University), Chennai, India Salam Shuleenda Devi National Institute of Technology Mizoram, Aizawl, India Arijit Dutta KIIT University, Bhubaneshwar, Odisha, India Cu Nguyen Giap ThuongMai University, Hanoi, Vietnam R.J. Hemalatha Department of Biomedical Engineering, Vels Institute of Science, Technology and Advanced studies, (Deemed to be University), Chennai, India A. Josephin Arockia Dhivya Department of Biomedical Engineering, Vels Institute of Science, Technology and Advanced studies, (Deemed to be University), Chennai, India A. Keerthana Department of Biomedical Engineering, Vels Institute of Science, Technology and Advanced studies, (Deemed to be University), Chennai, India Ijaz Khan Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia (UTHM), BatuPahat, Malaysia Anand Kumar School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India Rabul Hussain Laskar National Institute of Technology, Silchar, Assam, India Sushruta Mishra KIIT University, Bhubaneshwar, Odisha, India Brojo Kishore Mishra C. V. Raman College of Engineering, Bhubaneshwar, Odisha, India Meena Moharana School of Computer Engineering, KIIT University, Bhubaneswar, India

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Contributors Tran Thi Ngan Faculty of Computer Science and Engineering, Thuyloi University, Hanoi, Vietnam Faridah Mohd Nor Department of Pathology, Faculty of Medicine Universiti Kebangsaan Malaysia (UKM), Medical Centre, Kuala Lumpur, Malaysia Manjusha Pandey School of Computer Engineering, KIIT University, Bhubaneswar, India Mamata Rath Birla School of Management (IT), Birla Global University, Bhubaneswar, India Siddharth Swarup Routaray School of Computer Engineering, KIIT University, Bhubaneswar, India K. Sangeethapriya Department of Biomedical Engineering, Vels Institute of Science, Technology and Advanced studies, (Deemed to be University), Chennai, India Vijender Kumar Solanki CMR Institute of Technology (Autonomous), Hyderabad, India Le Hoang Son VNU Information Technology Institute, Vietnam National University, Hanoi, Vietnam G. Srividhya Department of Biomedical Engineering, Vels Institute of Science, Technology and Advanced studies, (Deemed to be University), Chennai, India T.R. Thamizhvani Department of Biomedical Engineering, Vels Institute of Science, Technology and Advanced studies, (Deemed to be University), Chennai, India M. Thirumaran Department of CSE, Pondicherry Engineering College, Pondicherry University, Puducherry, India Do Nang Toan VNU Information Technology Institute, Vietnam National University, Hanoi, Vietnam Hrudaya Kumar Tripathy KIIT University, Bhubaneshwar, Odisha, India Tran Manh Tuan Faculty of Computer Science and Engineering, Thuyloi University, Hanoi, Vietnam Mohd Helmy Bin Abd Wahab Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia (UTHM), BatuPahat, Malaysia; Green ICT Research Group, Centre of Excellence Geopolymer and Green Technology, Universiti Malaysia Perlis, Perlis, Malaysia

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CHAPTER 1

Analysis of the role and scope of big data analytics with IoT in health care domain Sushruta Mishra1, Brojo Kishore Mishra2, Hrudaya Kumar Tripathy1, Arijit Dutta1 1

KIIT University, Bhubaneshwar, Odisha, India; 2C. V. Raman College of Engineering, Bhubaneshwar, Odisha, India

Chapter Outline 1. Introduction 2 2. Sources of health care data 2.1 2.2 2.3 2.4 2.5

2

Electronic health records (EHR) 2 Clinical text mining 3 Medical imaging data 3 Genomic data 3 Behavioral data 4

3. Tools and data analytics interfaces in medical and health care system 4 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 3.10 3.11 3.12 3.13 3.14 3.15 3.16

Advanced data visualization (ADV) 4 Presto 4 Hive 4 Vertica 4 Key performance indicators (KPI) 5 Online analytics processing (OLAP) 5 Online transaction processing (OLTP) 5 The Hadoop distributed file system (HDFS) 5 Casandra file system (CFS) 5 Map reduce system 5 Complex event processing (CEP) 5 Text mining 6 Cloud computing 6 Mahout 6 JAQL 6 AVRO 6

4. Health care with big data challenges

6

4.1 Issues related to policy and fiscal factors 4.2 Issues related to technology 7

6

5. IoT defined 7 Handbook of Data Science Approaches for Biomedical Engineering. https://doi.org/10.1016/B978-0-12-818318-2.00001-5 Copyright © 2020 Elsevier Inc. All rights reserved.

1

2 Chapter 1 6. 7. 8. 9. 10. 11.

IoT for health care 8 Challenges for IoT in health care 9 Evolution of big data in medical IoT 10 Advantages 12 Literature survey 13 Implementation of a real-time big data analytics of IoT-based health care monitoring system 15 11.1 Components and methods 16 11.2 Results and discussion 19

12. Conclusion 22 References 23

1. Introduction Data analytics acts as a major aspect for application in various fields. Data analytics have emerged as a vital tool for scientists due to the heightened presence of heterogeneous and unstructured data around the world. Scalable data analytics techniques are needed; in medical sectors, massive data is regularly aggregated in several organizations. These data sources act as a resource in deriving insights for enhancing care delivery and minimizing waste. The volume and complex nature of such data is a challenge in analyzing and applying in a real-life health care environment.

2. Sources of health care data Datasets gathered in health care domains includes quantitative and qualitative data. Quantitative data is of quantifiable nature and used for comparison purpose. Examples include weight, age, temperature, or any other discrete variables. Qualitative data are nonnumerical in nature which is used to represent health related problems. Some examples include male/female or smoker/non-smoker etc. Data sources in medical field include scientific data and clinical data. Clinical data include data related to clinical surveys or epidemiological based information. Scientific data denotes data related to bench sciences. Data recorded and collected in medical domain are of primary and secondary in nature. Primary data refers to the individual person or a group to collect and analyze the data. This collected data may be used for research queries. Secondary data is dependent on the existing data which are already available and is utilized for other purpose. These data are used to answer research-based questions. Fig. 1.1 highlights the health care sources of data samples.

2.1 Electronic health records (EHR) This is an important source of data in medical field. Electronic health records (EHR) refers to the digital records of patients. Here the data can be accessed from anywhere and

Analysis of the role and scope of big data analytics with IoT in health care domain 3 Medical Imaging Data

EHR data

Clinical Text Mining

Sources of Health care Data

Genomic Data

Behavioral Data

Figure 1.1 Health care data sources.

whenever required. It may be structured or unstructured. In structured data, all records are properly captured and categorized in a database. But unstructured data records are vague and inconsistent which are presented in the form of static pages of health information. Examples include PDF files, emails, and digital images, audio, or video information. Ultimately these data are transformed into structured records.

2.2 Clinical text mining Health care records can be structured, unstructured, or can have text related information. Here text mining can be used to extract useful and sound information from huge raw data. Few text mining methods involve categorization and sentiment analysis. It is used for optimum targeting of drugs, precise disease diagnosis and efficient patient treatment. Natural language processing may be used in health care text mining.

2.3 Medical imaging data CT scans and X-rays belongs to unstructured data type. Picture Archival & Communication Systems is a system used to store and retrieve clinical imaging data records. In clinical retrieval process, images are deposited in repository of biomedical image data. These image-based data takes huge memory and is complicated in processing.

2.4 Genomic data These data handles DNA aspects in structural and sequential arrangement of various functionalities of genes. Specific software is required to store and process these data. A repository called genomic database comprises human genomes and association rules related to genomes. This repository determines the identical genetic symptoms influencing health and its associated diseases.

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2.5 Behavioral data The source of behavioral data lies in mobility-based sensor data associated with social network. There exist some social networks which keep track of diseases and their symptoms. Accordingly, they offer better treatment process based on the symptoms involved. Similarly, sensors can be deployed to gather and aggregate disease related data from patients in a health care institutions.

3. Tools and data analytics interfaces in medical and health care system There exist various tools and applications which are used to determine the progress in clinical data analysis. Some of the widely popular tools used are presented below.

3.1 Advanced data visualization (ADV) ADV is useful to deal with several types of data. It changes from line chart to standard bars. It is quite easy to use. It offers wide support to analysts in data exploration. It produces very optimum results and used to extract medical hidden patterns in health care data.

3.2 Presto Presto is a distributed SQL Query engine used in analyzing massive quantity of clinical data. It is applied in a large-scale analysis where data analysis can be done without significant delay.

3.3 Hive It is also applied to deal with large scale data records. It is not so fast like Presto tool. In fact it performs all Excel sheet tasks effectively. Many industries prefer Hive for medical records storage and retrieval.

3.4 Vertica This tool is identical to Presto and is utilized in processing huge amount of clinical based data which may be further used for data analytics. It is cost effective and its architecture is simple. It is very scalable in nature. It is advantageous in reducing operational costs, speeding up health care reports and documentation thereby helps in analyzing health patterns of patients.

Analysis of the role and scope of big data analytics with IoT in health care domain 5

3.5 Key performance indicators (KPI) This represents a procedure which makes application of electronic health care records in determining inventions and practices of human beings. Patients who are more vulnerable to hospital environment may be subjected to KPI tool to get better results.

3.6 Online analytics processing (OLAP) Here the data is organized in multidimensional patterns which perform statistical computation at a great speed. It amplifies data integrity constraints and establishes better quality control. It keeps track of health care records and helps in disease diagnosis.

3.7 Online transaction processing (OLTP) OLTP and OLAP are interrelated to each other. This tool is useful in processing registration of patients, analyze various operations of patients and result review analysis.

3.8 The Hadoop distributed file system (HDFS) The performance of clinical data analytics is improved by the use of HDFS which partitions huge data sets into relatively smaller ones. These smaller data samples are distributed across entire system. It removes redundancy of data. It acts as a diagnosis assisting tool and is used to monitor and detect fraud elements and patients symptoms.

3.9 Casandra file system (CFS) It is very much identical to HDFS. This file system is designed to handle analytical operations and is fault-tolerant.

3.10 Map reduce system This system deals with massive amount of data. It partitions the chore into subchores and aggregates its output. It efficiently integrates various operational computations into the system. It tracks every server where the chore is being done. The main benefit lies in its higher degree of parallel tasks.

3.11 Complex event processing (CEP) It is a recent addition in medical sector which helps in monitoring different phases of patient. Complicated event processing is interlinked in real-time analysis.

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3.12 Text mining In medical field text mining systems may act as an advantage in examining medical records from medical centers. It can be useful in devising treatment plans which can develop many protocols. Further treatment of patients can be undertaken relating to such developed guidelines.

3.13 Cloud computing Cloud computing technology offers higher flexibility in medical field as far as dealing with adaptive variations and health care updates are concerned. It is an addition to clinical sector by reducing the medical costs, enhancing the productivity an optimizing data analysis task.

3.14 Mahout Mahout is an apache-based project which is intended in developing applications to improve clinical data analytics on Hadoop systems.

3.15 JAQL JAQL is a procedure-oriented query language useful in processing massive amount of data. Parallel processing of data is feasible by converting higher level queries into lower level ones. It is well suited to work with Map reduce functions.

3.16 AVRO AVRO is effective in encoding and serialization of data. It enhances data semantics by specifying types of data samples used, semantics and its schema.

4. Health care with big data challenges The challenges can be categorized into two types:

4.1 Issues related to policy and fiscal factors In the age of money for service scenario, the medical experts can get paid only when they have a face to face interaction with their patients. It acts as a bottleneck to promote new technologies that encourage interaction without physical presence of patients. Moreover, as we go further away from direct interaction-based models, where there are more financial risks are involved there is more scope of using recent advanced technologies where

Analysis of the role and scope of big data analytics with IoT in health care domain 7 unnecessary face to face interactions may be avoided. In such cases, face to face interactions with patients are quite expensive while use of advance technologies impacts a positive influence in health outcomes of people.

4.2 Issues related to technology One of the largest technical obstacle to achieve this mission is the status of medical data. Developed by EHR systems, medical based data records are highly segmented into organization-based silos. Maximum effort is given to deal with this exchange of individual data records in between silos with the use of standard code sets and message structure. But it fails to solve the data fragmentation issue. Recently people in medical arena are visualizing the future generation of medical field lies in data aggregation and not just sharing copies of patient records. The data can be made relevant and useful only when data can be gathered from heterogeneous sources and further normalizing the gathered data and resolving the information with unique identifiers of patients. There are two main benefits of aggregated data. •

•

It resolves the interoperability issue. Organizations are no more required develop data bridges and convert the data between proprietary systems. They just need to connect data sources to a common API module. This data aggregation forms the basis of effective artificial intelligence technology. It provides adequate flexibility thereby allowing artificial intelligence and machine learning to operate efficiently in real-time manner.

5. IoT defined IoT refers to a computational notion to describe the concept of daily physical objects which are connected to internet such that they are able to identify and distinguish themselves from the rest. This methodology is acutely associated with RFID as the transmission technique. Besides this, it involves sensor and wireless technologies or QR codes. The significance of IoT lies in the fact that the digital representation of object becomes more visible that the object itself. The object is no more interrelated to its user but also is related to its neighboring objects. Some crucial focus areas where IoT analytics can be successfully applied are: • • • • • •

Forecasting the agriculture production/manufacture Machine learning algorithms Failure prediction Predictive maintenance Supply and chain Frequent pattern mining.

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6. IoT for health care The main aim of technology in medical field is to connect health care experts with their patients through smart devices. This helps patients to be more aware of their issues and thus the diagnosis becomes effective. It empowers consumer with less inefficiencies and assists doctor in precise decision making. IoT in health domain serves two main purposes which are: • •

Enhanced management of disease providing better patient experience. Decreased medical costs to make it more affordable for a wider demographic population.

As shown in Fig. 1.2, a survey was conducted by Grand View Research where it is estimated that by the year 2022, IoT in health filed which covers the domain of medical equipment, services, and software is presumed to jump a whopping $300B market expansion. Central agency schemes are also likely to impact and encourage this value for customized smart health care. Fig. 1.3 highlights the architectural view needed in clinical IoT systems. It consists of three prime components, which include the device layer, which has the body area sensor network embedded into it; Internet-connected smart local access network, which is called a Fog layer; and a Cloud layer for cloud and big data service support. Several applications and firms provide services to various stakeholders within the system by the use of this model. Sensors attached to users are responsible to generate data which is made readily available to medical experts, family members and authorized firms enabling them to verify and validate the issues and diagnosis process at anytime from anywhere as well as

Figure 1.2 Coverage analysis of IoT in health care services.

Analysis of the role and scope of big data analytics with IoT in health care domain 9

Figure 1.3 Architectural elements of health care IoT systems [1].

assisting health care experts in intelligent decision making. In this new age of information, knowledge extracted from raw data is the need of the hour. This is the age of customers where their associations with health care world are a priority. At this juncture, apps and devices will be applied to develop a health-aware environment. Some of these devices include: • • • • • • • • • •

OpenAPS: closed-loop insulin delivery Continuous glucose monitoring (CGM) system Activity trackers during cancer treatment Connected inhalers Ingestible sensors Connected contact lenses Depression-fighting Apple Watch app Coagulation testing Arthritis: Apple’s ResearchKit Parkinson’s Project Blue Sky

7. Challenges for IoT in health care Prime objective of reputed IoT firms is to provide simple and powerful implementations to services of IoT and data handling facility. It helps designers to compose data analytics applications, visualization frameworks and health care IoT apps. Some of the critical capabilities that IoT organizations must be enabled are: •

Simple connectivity: An ideal IoT firm must be competent enough to provide ease of connection to devices thereby facilitating device management functionalities.

10 Chapter 1 • •

•

•

Easy device management: It enables enhanced availability of different assets and resources which lead to improved throughput and reduction in maintenance costs. Information ingestion: Intelligent transformation and storage of data is a vital factor In IoT. Information is ingested from distinct sources of data and then relevant information is extracted with the use of data analytics. Informative analytics: Proper analysis of raw information is important for optimal decision making and smooth operations. It is used in real-time analytics and monitoring present conditions to respond accordingly. Moreover an intuitive dashboard makes it more simple and effective to understand. Reduced risk: Act on warnings and isolate activities collected somewhere in the organizations from a unit console.

8. Evolution of big data in medical IoT The health care industry is the combination of different sectors. The sectors has the inclusion of medicines, precautionary with the statistical working of the properties and the socialization of care. Health care industries have been included with different nursing home, medical trials, outsourcing, health coverage, telemedicine and other charitable organization. These days the health care industry has been seen from a business point of view. The business provides good profits and other services for the people. Along with this there has been an inclusion of information and communications technology. The ICT cell has been able to provide the health care industry with the provision of different roles for the improvement of health care industry. The system would be able to help the existing system in the exclusion of medical errors. Information related to the system is generated from different sources. Sources for the data includes clinical trials, medicine, exercise, variable symptoms, prescription, laboratory report, insurance data and various other information related to the patient and doctors. The large amount data in the health care industry is growing in an exponential form with current data size in the order of petabytes. This immense growth of data has given rise to various problems related to storage, transfer, and computational analysis. This form of data can be analyzed and processed with the help of traditional relational database system. Moreover, traditional database system can only process structured data. Whereas the data stored in the form of big data is unstructured. With the invention of new and efficient mechanism for the storage and the accessing of the information the ICT would be able to help serve the society in a better storage. The process of implementing of ICT in the health care industry in termed as eHealth. Thus the implementation in the health care industry would help in the processing of data and consecutive analysis and the improvement of the decision-making process for the collection of better treatment solutions for the symptoms for the diseases. One of the top characteristics of the use of health care industry is the richness of data. With the recent development of the diagnostics and the treatment processes, the health care industry has

Analysis of the role and scope of big data analytics with IoT in health care domain 11 been used to quickly evolve the sector in the previous couple of decades. Several sources are used for generation of big data and the sources are considered as following: •

• •

•

Web and social media: Captured data from Facebook, LinkedIn, Twitter, blogs, and shared stories on social media. The data captured from health planning webpages, smartphone applications and other sources as well. Machine-to-machine device generated data: Remote sensor data, meter readings and other device readings are recorded as machine-to-machine device generated data. Biometric data and demographic data: Biometric data such as capturing data from retinal scans, X-ray images, fingerprints, handwriting, blood pressure and other similar type of data can be recorded as demographic sources as well. Human-generated data: Unstructured and semistructured data can be captured from manual data entering and some data examples are considered as EMRs, doctor’s Prescription, and other paper documentations.

Big data concept represents a technology that stores and analyses datasets of massive size such as petabytes, terabytes, exabyte, and zettabytes. Such measure of data that is generally out of human expertise to store data in manual effort; capturing and analyzing the data can include significant value for business and decision-making process. Hence, organizations today are making the most from Big Data technologies to store process and analyze the data generated. Three important characteristics or the three Vs of big data are volume, velocity, and variety (Fig. 1.4). The data can be structured, unstructured, or semistructured. Structured data has a predefined arrangement of data; examples include

Figure 1.4 Three Vs (velocity, volume, and variety).

12 Chapter 1 Healthcare Services Payors & Providers

101010101010 001000100010 001100110011

Massive Data Flows: Clinical Claims, Lab, Rx, Demographics, Benefits, DME, EHR, Clinical, Patient-Reported, and Supplemental Data Sources

Big Data Analytics Processing

Pharma Integration & Interoperability Impact Enablement

Devices

Diagnostics The MORE2 Registry© Database: Proprietary Design, Research and Functionality

Patients & Consumers

Figure 1.5 Big data analytics in health care: key components.

address data books, product information catalogs, and banking data. Unstructured data do not have predefined arrangement; examples include audio files, video files, text records, web sheets, computer programs, and social websites. Semistructured data is neither fully structured nor fully unstructured, and 80% of data is unstructured. Big data is interrelated with analytics and analytics is a familiar concept. Regression methods, simulation, and machine learning algorithms are the analytic techniques that were in use from past several years. The analytics carried out using the above-mentioned techniques for the unstructured data like email, computer programs and documents is understandable. However, the new thing is, as said above the advanced sources in computer technology and software’s like social media sites such as Facebook, Twitter, blogs, and many other business opportunities are generating enormous amount of data every minute. This has given rise to move toward advanced technology called Big Data analytics that supports many tools and technologies for the data analytics. Apart from just analyzing, the huge data there are many other benefits from big data technologies. Fig. 1.5 illustrates the key components of big data analytics in health care.

9. Advantages Here various benefits associated with big data in medical field are discussed. •

Reducing medical costs to achieve financial benefit: Cost of medical treatment can be significantly reduced with big data analytics. Also efficient data analysis provides information to physicians to determine populations at risk for ill-health. Hence proactive actions may be taken at prior. Big data can be applied accurately to analyze the scenarios where education and preventive actions are needed to get more healthy

Analysis of the role and scope of big data analytics with IoT in health care domain 13

•

•

•

•

•

•

populations at less cost. Treatment becomes more evidence related on applying big data analytics. Personalized medicine: Through genetic blue prints, medical experts can accurately predict the diseases and its risk factors. Big data further helps in personalization of medical drugs by determining proper treatments of patients. Patient treatment at early phase can reduce the risk of chronic disease factors. Strengthening preventive care: Prevention mechanism is far better than cure. With this rule, applying big data it is simpler to capture process and analyze symptoms of patients to offer a preventive care in an optimal manner. Wearable medical technologies: With the advance in technology, medical experts make better use of wearable devices thereby enhancing the quality of care and providing patients with more accessibility. Clinical trend analysis: By the usage of various analytical techniques which include machine learning and text mining, medical trend analysis and management of patients data become simpler with big data analytics. Detection and tracking patients: Various big data analytics methods like statistical clustering can be used to cluster group of patients suffering from different diseases. This technique applies available data samples. Also, patients can be tracked precisely to identify the regular patterns for treatment of disease. Analyzing efficacy of drugs: Studying drug efficacy may be done with electronic health record data. A comparative analysis of EMR data and hospital medical records are done and it is observed that cost of trials at random is higher than that of readily available HER based data records to analyze treatment procedures.

10. Literature survey Pagan et al. [2] in their work studied a power-aware huge deployment of a body area network to predict migraine activities of patients across Europe. It aimed to address issues in data acquisition and analysis in clinical sector. Adame et al. [3] developed an IoT monitoring model that combines both sensor network and RFID technology. It helps in tracking the location of medical resources and assets. It also keeps the patients informed about their body temperature, heart rate and movement. It is practically implemented in a hospital environment using back end servers with qualitative feedback analysis. A hybrid technique to sense and monitor locomotive movements by determining variations in wireless signal strength is proposed by Ammae et al. [4]. With the change in signal strength, it allows an unobtrusive method of determining the quality of sleep using maximum likelihood linear regression model. It was evaluated with the usage of 60 iterations of real-time information gathered from 6individuals and the result obtained was very positive and efficient. Woo et al. [5] focused on fault-tolerant medical data services by developing a fault-tolerant and reliable IoT system model. Here the gateways are

14 Chapter 1 interlinked to develop a daisy chain of reliability where the replica of the predecessor gateway can be stored in the daisy chain. It enabled the user to simultaneously recover from a two gateway faults. Rahmani et al. [6] presented a smart e-health care gateway at the network edge in an architectural configuration of a fog computing. Farahani et al. [1] in his article discussed regarding the challenges and scope of IoT in the health care field. A system model was proposed that migrated from hospital-oriented centers to people oriented centers with the help of IoT infrastructure. In Refs. [7,8], an IoT framework for elderly persons was proposed and implemented by observing several physiological features. An IoT-based intelligent wallet system was developed in Ref. [9] which is associated with every individual in order to store signals and wallet shares. A wireless sensor network based IoT model was presented in Ref. [10] that reduced the power consumption at the sensor nodes which enhanced the network lifetime [11]. discusses the impact of IoT concept with respect to the overall geographical range. Developed countries are rapidly progressing in IoT field but it is the less developed regions where IoT technology is crucial for the overall development of humanity. Authors in Ref. [12] developed a heart monitoring gadget with the help of remote sensors and advanced smart phone technology. It sends an alarming signal to the medical experts and family members in case any discrepancies are observed in the patient. The security and privacy concerns in IoT can be properly addressed by the usage of body sensor network technology which is illustrated in Ref. [13]. Authors in Ref. [14] present an elaborate analysis on the collaboration of cloud and IoT together. These two technologies can work together to offer efficient health care monitoring system model where huge amount of data can be processed and results can be obtained in real time more accurately [15]. developed and analyzed a pervasive surveillance system with mesh topology which is used as data compression processing system for patients in hospitals. Data are aggregated and stored in cloud server. Real-time data in the form of images and video are generated and analyzed. Any variation in data recorded is immediately monitored and appropriate actions are taken on patients [16]. discusses the significance of data quality and reliability as two important factors in health careebased IoT applications. The clinical analytics with the use of an effective anomaly identification model was proposed to detect any early and prompt inconsistencies of dominant diseases. Authors in Ref. [17] proposed a health care system for elderly patients. It employed a central unit model which facilitated in decision making which could detect any critical anomalies in elderly people based on the data generated by the sensor units. At the end of analysis in emergency scenario, the system model transmits an alert signal to an emergency control unit. Sushruta Mishra et al. [18] proposes a machine learning-system model based on biologically inspired computation for primary tumor classification. Genetic algorithm was the attribute optimization technique used in the study. Authors in Ref. [19] presented a social network analysis framework for big data analysis on telecommunication domain. similarly various research works are being carried out in relation to big data analytics and IoT filed with respect to medical data analysis.

Analysis of the role and scope of big data analytics with IoT in health care domain 15

11. Implementation of a real-time big data analytics of IoT-based health care monitoring system Complicated tools integrated with IoT are fruitful for the medical experts in monitoring huge health care related data records so as to keep track of their patient’s health condition. According to the signals transmitted by the IoT system, health status of patients is continuously tracked and in case of any abnormalities, alert signals are sent to the physicians. For example, if the glucose level of a patient drops, the system will transmit an alert message to the doctor who can interact with his patient and take appropriate actions. This data is aggregated from patients and deployed in cloud. The cloud environment makes it feasible to gather and aggregate data rapidly and more accurately. The proposed system model integrates various entities like medical amenities, physicians, vital medical services, patients and other technologies to extract the relevant value form the gathered medical data records. Salient features of the proposed system model include the following: • • • • •

Sensors are used to collect health data of patients which are further used for processing and communication purpose. Intel Galileo Gen2 which acts as medical IoT agent are utilized in analyzing sensor related data and used for storage in cloud for analysis. Data analytics of medical related records with the help of the map reduce technique in cloud. Graphical user interfaces controlled by medical monitors for smooth progress. Smart phones embedded with GPRS and GSM connections.

Big data analytics for health care based IoT architecture is represented in Fig. 1.6. The health proxy played by the IoT agent is coupled with the heart rate sensor, blood pressure sensor and humidity sensor. The medical metrics sensed by the system model are employed and integrated within cloud. The cloud concept is used here to analyze such huge medical data generated from various patients across distinct medical organizations. Historical data analysis of medical information for patients is feasible with the stored data in cloud. The physician’s smart phone is used to connect directly to the IoT agent through GPIO pins and collect health careebased data of patients. Appropriate medical help may be provided if any critical medical condition is identified in patients. The proposed health care monitoring model with IoT can also be further integrated with smart phone interfaced with it. The system model shown in the figure offers connectivity to cloud thus delivering end-to-end customer value. Sensor data analytics are executed inside the cloud using HDFS file system and MapReduce method. It is used for storing and processing health care related sensor information.

16 Chapter 1 GALLILEO GEN 2

IOT Agent

IOT Analytics HTTP

GPRS/GSM

Figure 1.6 Proposed IoT architecture.

11.1 Components and methods The developed clinical monitoring model used various types of sensors such as finger moisture sensors, blood pressure sensors and heart beat rate sensors. These sensors collect and aggregate data from patients. This health-monitoring model uses various physiological signals generated from patients. Intel Galileo which acts as an IoT agent is an IBM registered device is selected for implementation purpose. Sensor data values are recorded with the use of Arduino programming. With the help of Intel Galileo Gen2, interfacing with outside world is also feasible. Fig. 1.7 illustrates a heart beat sensor while Fig. 1.8 shows moisture sensor implemented in our developed work. A PCB antenna designed with an industrial based standard interface. ATWIN Quad-band GPRS/GSM shield acts as a wireless module base, which is of very high quality performance based on UCL2 interface. It is a small dimension-based SMT package with less power consumption with a double-band package. It has provision of SMS, voice, data, and fax applications for medical experts. It is shown in Fig. 1.9. • • • •

IoT Proxy’s D0 is connected to Rx of hardware serial port IoT Proxy’s D1 is connected to Tx of hardware serial port IoT Proxy’s 5V are connected to 5V of hardware serial port IoT Proxy’s GND is connected to GND of hardware serial port

Analysis of the role and scope of big data analytics with IoT in health care domain 17

Figure 1.7 IEEE 802.15.4 sender and receiver with heart beat sensor.

Figure 1.8 Intel Galileo Gen2 with moisture sensor.

An unlocked mini-SIM (size of 2 FF) card is associated within it. The IoT Proxy lies in the shield. No extra wiring is necessary. The Serial port select jumpers to the hardware serial position are set as follows: • •

Set J1 to connect Rx to MTx Set J2 to connect Tx to MRx

Fig. 1.10 shows the SIM card inserted into Intel Galileo. The components required for the Intel IoT Developer Kit used in this paper needs to connect the IoT proxy (Intel Galileo Board) over Wi-Fi connection. Through available miniPCIe slot IoT agent provides cloud based connectivity with sensors supporting Wi-Fi service which leads to development of IoT health-monitoring model.The IoT agent is able to abstract the complexities of the

18 Chapter 1

Figure 1.9 SMT package inserted into Intel Galileo with PCB antenna.

Figure 1.10 SIM card integrated to Galileo Gen2.

connectivity to the cloud. This process helps in the focusing of the application development according to the proposed solution including the health care sensors. The agents’ works in the formatting of data and the security protocol for registering sensors in cloud. With provision of end-to-end service of IoT solution from Intel, it has been implemented in the proposed system. The proposed system would have quick management, connectivity, and protection of the sensors. Physiological metrics like heart beat levels are gathered from sensors and are used for storage on a web cloud server for processing. “Thingspeak” cloud is used in this implementation work. The readings of finger moisture level are recorded at regular intervals and are shown in Fig. 1.11. The medical sensor data records generated in

Analysis of the role and scope of big data analytics with IoT in health care domain 19

Figure 1.11 Deployment of finger moisture in Thingspeak cloud.

Thingspeak cloud are mapped onto Hadoop Distributed File System (HDFS). HDFS acts as the storage for Hadoop cluster. The medical data is split into small divisions and are made available across several servers in cloud. Map reduce programming is used to perform the computational distribution of sensor data. Data is gathered by distributing various subtasks in each server. In the cloud environment, all the server nodes are tracked with the use of map reduce. The map reduce technique with its classes and methods split the large data file into relatively smaller divisions which are further mapped in the cloud. These smaller data partitions are parallel executed to determine the final output. As a result the execution time is reduced. Finally the result of these sensors of map reduce is published to all mobile phones with medical experts after appropriate authentication.

11.2 Results and discussion The patients act upon the heart beat arte sensors and the IoT device records the data. This data is implemented with Thingspeak cloud. This procedure is highlighted in Fig. 1.6. Also the recordings of moisture sensors are displayed. A command tool called Hive is used to retrieve the data recordings of body sensor. Fig. 1.12 depicts the data analytics gathered from the body pressure with Hive query command. It is clearly seen that the response time period of the proposed health care system is very prompt as the completion of the query using Hive command requires only 1.05 s after the Map Reduce processing is over in the cloud, the aggregated sensor information is transmitted to the health care

20 Chapter 1 hive> CREATE TABLE xml7(Level map, Pressure map) >

ROW FORMAT SERDE

'com.ibm.spss.hive.serde2.xml.XmlSerDe' >

WITH SERDEPR0PERTIE5 (

>

"column.xpath.Level"="/Units/Level",

>

"column.xpath.Pressure"="/Unlts/Pressure"

>

)

>

STORED AS

>

INPUTFORMAT 'com.ibm.spss.hive.serde2.xml.XmlInputFormat'

>

OUTPUTFORMAT

'org.apache.hadoop.hive.ql.io.HiveOutputFormat' >

TBLPROPERTIES (

>

"xmlinput.start"="",

>

Figure 12. Body pressure with Hive query command "xmlinput.endn=""

>

);

OK Time taken: 1.056 seconds

Figure 1.12 Body pressure with Hive query command.

monitoring system which is highlighted in Fig. 1.13. The smart phone device may be connected to the interface directly. Apart from this, the IoT agent can also communicate with the smart phone by the usage of SMT package and SIM card of the device. This condition is illustrated in Fig. 1.5. The alerting model is developed which is based on the optimal threshold computation which is seen in Fig. 1.14. Once arrhythmia is diagnosed in patient, the alert system immediately sends an alert signal. The model reads the physiological metrics which provides the alert signals after the threshold value is compared. A self-adaptive alert system has been included in the proposed health care system which generates notifies the medical experts about their patients in case any emergency arises. The threshold data values concerning the heart beat rate alert signals are illustrated in Table 1.1.

Analysis of the role and scope of big data analytics with IoT in health care domain 21

Figure 1.13 Health-monitoring Interface.

If HR > 100

No

If HR < 60

No

HR is normal

Yes

Yes

Tachycardia

Bradycardia

Arrhythmia detected

Figure 1.14 Flow chart for alert.

Table 1.2 depicts the data gathered from the alert system model implemented in the proposed health care system. A comparative analysis was carried out and it was determined that the heart beat monitoring has been enhanced with the proposed model. It is measured that the average time taken to transfer the alert signal between the sender and the receiver in presented IoT model is found to be 35 s and this time is within 3G network recorded in Table 1.2. As per big data analytics requirements, the overhead is performed on the heartbeat rate, and thereafter the alert signal is raised.

22 Chapter 1 Table 1.1: Threshold data of heart beat rate alert signals. Sinus rhythm type

Threshold data of heart beat rate

Normal Tachycardia Bradycardia Sinus rhythm type Normal Tachycardia Bradycardia

60 < HR < 100 (beats/minute) HR > 100 (beats/minute) HR < 60 (beats/minute) Threshold value of heart rate 60 < HR < 100 (beats/minute) HR > 100 (beats/minute) HR < 60 (beats/minute)

Table 1.2: Average data transmission time.

Alert for

Mean time in send and receive alert in Wi-Fi (H:M:S)

Tachycardia Bradycardia

00:00:29 00:00:30

Mean time in send and receive alert in 3G network (H:M:S) 00:00:58 00:00:59

Mean time in send and receive alert in proposed IoT system (H:M:S) 00:00:35 00:00:38

12. Conclusion More new technologies are rapidly becoming useful in the health care sector, including devices and models that regularly monitor health parameters and other devices that keep track of real-time medical information. Due to increases in Internet speed and the wide availability of smart phones, patients as well as doctors are using mobile-based applications to regulate their health requirements. Integration of big data analytics with IoT technology plays a vital role in this health care domain. In this chapter we have drawn a detail analysis and discussion about the role and scope of big data analytics and IoT technology in medical field. Later a case study illustrating an IoT-based medical monitoring system model with Big data analytics is presented and its implementation is also highlighted. Here huge quantity of heartbeat data records of patients are gathered and respective medical expert is required to segregate the data according to patient. Intel Galileo Gen2 is the IoT-based agent used in the above implementation to integrate clinical data of concerned patients with Thingspeak cloud. Hadoop framework is applied to process this massive data of patients over cloud. It was observed that the response time was much less without significant delay. Also, it can be deployed in real-time environment for health care monitoring of patients. Clinical metrics coordination is done with the usage of GPRS/GSM connection abilities of Intel Galileo Gen2 through alert signals. Physicians can take benefits from this developed health care system providing appropriate information to appropriate patient at appropriate time. Hence, effective and timely diagnosis can be provided to patients with minimum response time.

Analysis of the role and scope of big data analytics with IoT in health care domain 23

References [1] B. Farahani, F. Firouzi, V. Chang, M. Badaroglu, N. Constant, K. Mankodiya, Towards fog-driven IoT eHealth: promises and challenges of IoT in medicine and healthcare, Future Generation Computer Systems 11 (2017). [2] J. Pagan, M. Zapater, J.L. Ayala, Power transmission and workload balancing policies in eHealth mobile cloud computing scenarios, Future Generation Computer Systems 3 (2017). [3] T. Adame, A. Bel, A. Carreras, J. Meli-Segu, M. Oliver, R. Pousa, Cuidats: an RFID-WSN hybrid monitoring system for smart healthcare environments, Future Generation Computer Systems 7 (2017). [4] O. Ammae, J. Korpela, T. Maekawa, Unobtrusive detection of body movements during sleep using Wi-Fi received signal strength with model adaptation technique, Future Generation Computer Systems 16 (2017). [5] M.W. Woo, J. Lee, K. Park, A reliable IoT system for personal healthcare devices, Future Generation Computer Systems 15 (2017). [6] A.M. Rahmani, T.N. Gia, B. Negash, A. Anzanpour, I. Azimi, M. Jiang, P. Liljeberg, Exploiting smart ehealth gateways at the edge of healthcare internet-of-things: a fog computing approach, Future Generation Computer Systems 5 (2017). [7] S.M. Riazul Islam, D. Kwak, M.D. Humaun Kabir, M. Hossain, K.-S. Kwak, The internet of things for health care: a comprehensive survey 3 (2015) 678e708. [8] A. Khanna, P. Misra, The Internet of Things for Medical Devices Prospects, Challenges and the Way Forward, 2017. [9] L. Yu, Y. Lu, X.J. Zhu, Smart Hospital Based on Internet of Things, 2012. [10] R. Singh, A proposal for mobile E-care health service system using IOT for Indian scenario, Journal of Network Communications and Emerging Technologies (JNCET) 6 (2016). [11] A. Mathew, S.A. FarhaAmreen, H.N. Pooja, V. Aakriti, Smart disease surveillance based on Internet of Things (IoT), International Journal of Advanced Research in Computer and Communication Engineering 4 (5) (May 2015). [12] S.C. Padwal, S.V. Kurde, LongTerm environment monitoring for IOT applications using wireless sensor network, International Journal of Engineering Technology, Management and Applied Sciences 4 (2016). [13] P. Gope, T. Hwang, BSN-care: a secure IoT e based modern healthcare system using body sensor network, IEEE Sensors Journal 16 (5) (March 2016). [14] Y.-P.H. Haobijam Basanta, T.-T. Lee, Intuitive IoT e based H2U healthcare system for elderly people, in: IEEE 13th International Conference on Networking, Sensing and Control Mexico, April 2016. [15] A.P. Plageras, K.E. Psannis, Y. Ishibashi, IoT e based surveillance system for ubiquitous healthcare, in: IEEE 11th International Conference, Greece, 2016. [16] A. Ukeil, S. Bandyoapdhyay, C. Puri, IoT healthcare analytics: the importance of anomaly detection, in: IEEE 30th International Conference on Advanced Information Networking and Applications, India, 2016. [17] P. Gupta, D. Agrawal, J. Chhabra, IoT based smart HealthCare kit, in: IEEE International Conference on Computational Techniques in Information and Communication Technologies, India, 2016. [18] S. Mishra, H. Kumar Tripathy, B.K. Mishra, Implementation of biologically motivated optimisation approach for tumour categorisation, International Journal of Computer Aided Engineering and Technology 10 (3) (2018). [19] S. Mishra, H. Kumar Tripathy, B.K. Mishra, M. Mishra, B. Panda, Use of social network analysis in telecommunication domain, Modern Technologies for Big Data Classification and Clustering (2017) 152e178.

CHAPTER 2

Automated human cortical bone Haversian canal histomorphometric comparison system Hadi Abdullah1, Muhammad Mahadi Abdul Jamil1, Ijaz Khan1, Mohd Helmy Bin Abd Wahab1, 2, Faridah Mohd Nor3 1

Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia (UTHM), BatuPahat, Malaysia; 2Green ICT Research Group, Centre of Excellence Geopolymer and Green Technology, Universiti Malaysia Perlis, Perlis, Malaysia; 3Department of Pathology, Faculty of Medicine Universiti Kebangsaan Malaysia (UKM), Medical Centre, Kuala Lumpur, Malaysia

Chapter Outline 1. Introduction 26 2. Sample collection 31 3. Sample preparation 33 3.1 3.2 3.3 3.4

Specimen defatting 34 Sectioning of bone specimen 34 Specimen grinding and polishing 36 Glass slide mounting 39

4. Difficulties in sample preparation 4.1 4.2 4.3 4.4 4.5 4.6

40

Trapped air bubbles in the glass sample Thick bone slice 42 Uneven thickness 42 Broken section 42 Dirty specimen 43 Fragile bone specimen 43

5. Image acquisition 44 6. Microstructural parameter selection 6.1 6.2 6.3 6.4 6.5 6.6

40

46

Haversian canal number (hcn) 46 Mean Haversian canal area (hcm) 46 Total Haversian canal area 47 Mean Haversian canal radius 47 Mean Haversian canal perimeter 48 Percentage area covered by Haversian canal (hcpar) 48

Handbook of Data Science Approaches for Biomedical Engineering. https://doi.org/10.1016/B978-0-12-818318-2.00002-7 Copyright © 2020 Elsevier Inc. All rights reserved.

25

26 Chapter 2 7. Inclusion and exclusion criteria 48 8. Statistical tests 48 9. Automated comparison system 49 9.1 Comparison test selection

49

10. Automated system design 50 11. Sex comparison without age groups 11.1 11.2 11.3 11.4 11.5 11.6 11.7

Sex Sex Sex Sex Sex Sex Sex

comparison comparison comparison comparison comparison comparison comparison

12. Race comparison without age groups 12.1 12.2 12.3 12.4 12.5 12.6 12.7

Race Race Race Race Race Race Race

52

hcm 53 hca 54 hcr 54 hcp 56 hcn 56 hcpar 57 discussion 57

comparison comparison comparison comparison comparison comparison comparison

58

hcm 59 hca 60 hcr 61 hcp 61 hcn 62 hcpar 62 discussion 64

13. Conclusion 65 References 65

1. Introduction Information extraction from skeletal remains has been one of the key focus in physical anthropology and forensic sciences. The major components of this information are age at death, sex, race, and stature. In archeology this information can play a vital role in creating demographic profiles, which can lead to cause identification, distribution, and occurrence of diseases for paleopathology. In forensic sciences this information is particularly helpful in human identification from skeletal remains. The identification techniques can be categorized into two main groups which can be applied based on the state of obtained skeletal remains. Macroscopic or morphological techniques can provide information if major parts of the skeleton are obtained. The skeletal remains which can be analyzed in morphological techniques are mainly compose of skull, pelvis, ribs, and long bones. These techniques utilize the morphological degenerative process. Human bones explicit a linear relation with increasing age. However, the relation weakens when humans reach to maturity and after maturity the degenerative process makes it inefficient. In archeological and forensic sciences, intact skeletal remains are not consistently available. In some cases, only fragmented bones are obtained which makes it difficult to apply

Automated human cortical bone Haversian canal histomorphometric comparison system 27

Figure 2.1 Bone fragments of a burned bison at the Mile Canyon [1].

morphological techniques. Fig. 2.1 shows the bone fragments of a burned bison at Mile Canyon. Microscopic or histological techniques on the other hand are not bound to the main skeletal components. Researches have been carried out to find a relation between fragmented bone microstructures with age at death, sex, race, and stature [2e4]. Histological analysis of cortical bone to obtain the relation of microstructures with age started a few decades ago. Kerley in 1965 presented age regression equation to calculate age at death using cortical bone microstructures. Although work has been done on different parts of the world, there are no standard microstructures of cortical bone that can be analyzed globally [3]. This is because bone microstructures differ in human belonging to different races and region of the world [5]. Environment and ancestral background play an important role not only in morphology of the human bones but also effects the microstructures in cortical bones [6]. This led for stand-alone studies for humans belonging to various races and region which can be helpful in forensic investigations, archeological explorations, and contribute for future comparisons. Human beings are related to three major ancestral backgrounds caucasoid, mongoloid, and negroid. Fig. 2.2 shows a comparison of skull morphology of humans related to their ancestral backgrounds. The Malaysian population is divided into three main ethnic groups, Malay, Chinese, and Indian [7,8]. According to the statistics provided by Jabatan Perangkaan Malaysia, 89.7% of the total Malaysian population are Malaysian citizens. Malay cover 68.6%, while Chinese are 23.5%, and Indian race covers over 7%. Fig. 2.3 shows the Malaysian population division with respect to race.

28 Chapter 2

Figure 2.2 Morphological comparison of caucasoid, mongoloid and negroid skulls [5].

Figure 2.3 Ethnic composition of Malaysian population [9].

Over the past decade, research on morphological identification from skeletal remain has been done on Malaysian population. This includes race, stature, sex and age at death identification. In 2017 a comparison was performed on the human skull morphology to observe sex related differences in Malaysian population. The work was divided into two parts where two major sections (crania and mental foramen) of the skull were analyzed. Fig. 2.4 shows human skull bone sections. This study provided promising results where it can provide an accurate sex determination of 78.2%e86.2% in Malaysian population [10,11]. In a similar study, stature was estimated from the lower limb’s anthropometry. A regression equation was developed which can be used on Malaysian population for stature estimation [12].

Automated human cortical bone Haversian canal histomorphometric comparison system 29

Figure 2.4 Human skull, mandible, and cranium [10].

Histological identification has also been focused for Malaysian population. Research has mainly been done on age estimation from long bone fragments. In 2014, an age regression equation was presented to estimate age at death for Malaysian males with an accuracy of
10.94 years [13]. A comparison was done between animal and human samples in 2015. This research provided an identification accuracy of 95.3% for human and 63.1% for nonhuman [14]. Another research was done on age estimation using Haversian canal-based microstructural parameters for Malaysian females [15]. These researches provide basic building blocks for the histological methods, which need more research on Malaysian samples to provide better knowledge of histological differences in Malaysian races and sexes [16,17]. Histological methods are still in developing stage in Malaysia [18e20]. The age regression equation developed in 2014 by F.M. Nor used heterogeneous samples and there is no comparison available between the three main races. Sex-based comparison was also performed using heterogeneous samples and no age gradation was used. From age regression equation by F.M. Nor, it can be observed that the selected microstructures increased with respect to age. Bone morphological and histological changes with respect to progressing age is shown in Fig. 2.5. Without using age graded groups, no difference could be observed in bone histological sex comparison performed by F.M. Nor [22]. Microradiography has been used in various researches which provides microscopic images with more visible microstructures [6,23]. Image processing techniques have been applied to extract microstructures without effort. Microradiography or any other alternative automated system is not yet used on Malaysian samples. The process of manual measurement of microstructural parameters has made the work tedious and consumes great

30 Chapter 2

Figure 2.5 Morphological changes in the human skull with progressing age from new born to elderly adult [21]. Histological changes with age progression in microstructures of cortical bones in Malaysian citizens are demonstrated under the relative human skull morphological changes. The microscopic images are obtained from thin bone slices at 10 times magnification from human bone specimen obtained from 22, 35, 58, 76, and 92 years old.

amount of time (which is very precious specially in forensic cases). Manual measurements involve human errors, which can make the analysis less accurate. Malaysia is a culturally rich country with ethnic and religious values. Due to reservations and availability of cases in forensic center in Malaysia, homogeneity and adequate sample number for analysis is at times, difficult to achieve. To overcome these limitations, there is a desperate need of a computer aided system. These systems will not only ease the process of microstructures measurement but also keep a readily available data base for further research and analysis. The remodeling process of human bone forms different microscopic structures in bones. These structures persist in the bone at microscopic level even after the death of a person [4,24]. In histological analysis of bones these microstructures are studied and based on their structure human information such as age, sex, stature, and racial background are predicted. A big challenge in bone microscopic analysis is differences in selected microstructural parameters and their definitions in literature. The result of overall research of bone histological study and comparisons strongly lies in microstructures selection. These microstructures can be divided into two major categories, observed and derived parameters. Selection of microstructures and region of microscopic images varies in previous researches (Table 2.1). Most of the microstructures are either observed or derived measurements of osteons and Haversian canals.

Automated human cortical bone Haversian canal histomorphometric comparison system 31 Table 2.1: Imaging location, bone type, and area selected by researchers. References

Imaging location

Region/area

Bone

Kerley, 1965 [25]

USA whites

Femur, tibia, fibula

Age regression

IJ Singh, DL Gunberg, 1970 [26]

Portland

Femur, tibia

Age regression

DD Thompson, 1979, 1981 [27,28]

Femur

Population comparison

David B Burr, 1990 [29]

USA whites and Eskimos from St. Lawrence, North Alaska and Canada Native American and USA whites

Femur

Age regression

Ericksen, 1991 [30]

USA whites

Femur

Age regression

DM Mulhern, 1997 [31]

Medieval Nubian population from Kulubnarti, Subanese Nubia Australia

Femur

Population comparison

Femur

Population comparison

C David L Thomas, 2005 [6]

Australia

Femur

Population comparison

F Nor, 2009 [13]

Kuala Lumpur, Malaysia

Femur, tibia, ulna, radius, humerus, fibula

Age regression

HM Britz, 2009 [33]

Australia

Femur

Population comparison

C Hernandez, 2012 [34]

USA whites

Femur

Age regression

This study

Kuala Lumpur, Malaysia

Femur, tibia, ulna, radius, humerus, fibula

Population comparison

KL Bell, 2001 [32]

Purpose

2. Sample collection Bone specimens of 44 adults were collocated from Universiti Kebangsaan Malaysia Medical Center (UKMMC), Kuala Lumpur, Malaysia. All the collected specimens were from citizens of Kuala Lumpur Malaysia. The specimens were collected from individuals

32 Chapter 2 Table 2.2: Collected samples number with respect to sex and race. Race Chinese Indian Malay Indonesian Pakistani Myanmar Total

Male 16 9 1 2 1 1 30

Female 9 3 2 0 0 0 14

having no bone disease in previous medical records. The population of Malaysia is divided into three main ethnic groups Malay, Chinese (from south china) and Indian (from south India) [7]. The collected specimens were from 25 Chinese, 12 Indian, 3 Malay, 2 Indonesian, 1 Myanmar, and 1 Pakistan individuals. With respect to sex, 30 were males and 14 were females and age ranges from 22 to 91. The samples were not homogeneously numbered due to the ethnic and religious reservations and availability of cases in forensic medical center UKMMC. Table 2.2 shows the sample number with respect to race and sex. The bone types selected for this work were six human long bones comprising of humerus, radius, ulna, femur, tibia, and fibula (Fig. 2.6). The number of each bone type specimen collected is given in Table 2.3. According to previous research conducted on Malaysian

Figure 2.6 Long bones of the human skeleton selected for sex and race comparison of Malaysian samples [35e38].

Automated human cortical bone Haversian canal histomorphometric comparison system 33 Table 2.3: Number of collected specimen with respect to bone type. Bone type

Number

Humerus Femur Radius Ulna Fibula Tibia Total

11 9 9 1 8 6 44

samples, Haversian canal increases with respect to age [13]. This demonstrates the requirement of division of the samples into decades for better statistical observation.

3. Sample preparation To obtain microscopic images of the cortical bone, bone sample glass slides were prepared from the collected bone specimens. The specimens were collected from the midshaft of long bones with approximate thickness of 1 cm. Fig. 2.7 shows the left and right-hand side specimen of the femur and fibula taken from 68 years old female. The equipment utilized for bone slide preparation were: 1. 2. 3. 4.

Bright-field microscope: Nikon Eclipse Ts100 was used. Small heck saw. G-clamp. Glass microscope slides: Size 76  26 mm.

Figure 2.7 Left and right-hand side femur and fibula long bone specimen.

34 Chapter 2 Glass microscope coverslips: Size 24  40 mm. Small paint brush: Regular artistic paint brush. Tooth brush: Regular hard tooth brush. Mounting medium: Xylene mounting medium. Alcohol: 50% concentration. Fluid soap: Regular kitchen detergent. Tap and distilled water. Water bath: Wisebath fuzzy control system. Glass beaker: 1000 mL transparent. Fine quality water proof carborundum paper: grade 80, 120, 150, 180, 220, 240, 600, 1000 cc. 15. Paper cutting scissors. 16. Pipet: fluid capacity 4 mL and length 13 cm. 17. Fine point curved tweezer/forceps 5 in. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14.

3.1 Specimen defatting The specimens were de-fatted by placing in a glass beaker (filled quarter with distilled water) and placed in a water bath at 100 C for 15 min. This water bath loosens the fat which can be easily removed by using a brush. Once removed the samples are dried and cleaned with ethanol. Traditionally the specimens are de-fatted with diethyl ether solution. The samples are placed in this solution for 24 h [13,39,40]. This technique is used in which both cortical and trabecular bone are required for analysis. The solution dissolves fat and tissue while keeping trabecular part of the bone intact. The process of defatting is demonstrated in Fig. 2.8. In this research cortical bone was required and intactness of trabecular bone was not considered. However, the trabecular bone remained intact after the process of defatting.

3.2 Sectioning of bone specimen Since femur cortical thickness is greater than other bones and it is not possible to mount complete sample on one glass slide, specimen is cut into two halves using a mini handheld hacksaw (6 in.) and a G-clamp (4 in.). Each half is then cut into possible minimum thickness slice (Fig. 2.9). This process is not done with bone having small medullary cavity diameter. The bone types specimens which had greater medullary cavity diameter were femur and humerus while radius, ulna, tibia and fibula were mostly mounted as complete section on one glass slide. Some of the samples were observed to be fragile and had to be mounted on in sections on multiple glass slides.

Automated human cortical bone Haversian canal histomorphometric comparison system 35 (A)

(C)

(B)

(D)

Figure 2.8 (A) and (B) Bone specimen in a water bath. (C) Bone removal of loosened fat and tissue using a brush. (D) De-fatted bone specimen.

Figure 2.9 Slice cutting using mini hacksaw (6-in.) and G-clamp (4-in.).

36 Chapter 2

3.3 Specimen grinding and polishing The carborundum papers were cut into small pieces of approximately 3  3 in. and 1  3 in. The process of grinding was divided into two phases wet and dry. Dry grinding with hand although not recommended was done to save time. In the initial stage the specimen was thick and to grind it faster dry grinding was used. The specimen was placed on the 3  3 in. carborundum paper and 1  3 in. paper is wrapped around a glass slide such that it can be held by hand. The process starts by gently rubbing the glass slide and paper on top of the specimen in circular (clockwise and anticlockwise) motion and after every 1e2 min the specimen was flipped. The specimen was flipped using forceps. The grade of paper was started at 80 cc and was changed to finer grade after every 3e4 min. Grade 80, 120 and 150 cc were used in dry grinding. By the end of 150 cc the specimen thickness became approximately less than 1 mm. Dry grinding couldn’t be continued to obtain fine finished sample also the specimen becomes very fragile. Using dry grinding further could damage the specimen slice. This process took approximately 10e14 min. Fig. 2.10 shows the process of dry grinding. (A)

(B)

(C)

Figure 2.10 Dry grinding process. (A) Cutting the carborundum papers (80,120 and 150 cc) into 3  3 in. and 1  3 in. (B) Placing and flipping the specimen using forceps (C) clockwise and anticlockwise dry grinding.

Automated human cortical bone Haversian canal histomorphometric comparison system 37 (A)

(B)

Figure 2.11 (A) Dipping the carobrumdum base section in distilled water. (B) Wet grinding process.

Wet grinding was similar to dry grinding, the carborundum paper on the base and glass slide were soaked in distilled water and few drops of water were added after every 10e20 s during grinding. Wet grinding started with a paper grade of 180 cc and shifted to 220 and 240 cc after 3e4 min. Fig. 2.11 shows the steps of dipping carborundum base paper cut in distilled water. In wet grinding process the bone slice is dipped in distilled water after every 3e4 min to clean it from small particles during grinding. Fig. 2.12 demonstrates addition of water drops using pipets and dipping of bone slice in distilled water for cleaning. The bone slice in wet grinding becomes very delicate. It is handled with a small paint brush instead of forceps to prevent any damage to bone slice.

(A)

(B)

Figure 2.12 (A) Addition of water drops on the grinding platform using pipet. (B) Water bath to the bone slice in distilled water while handling it carefully with paint brush.

38 Chapter 2 (A)

(B)

Figure 2.13 (A) Bone slice while wet grinding using with 220 cc grade carborundum paper. (B) Bone slice lost its whitish bone color and appeared transparentdthe point where wet grinding was ended.

As soon as the specimen started to lose its whitish bone color and appeared to be transparent the grinding process was stopped. The wet grinding process took approximately 10e12 min. Fig. 2.13 shows the state of bone slice while wet grinding and the state at which wet grinding was stopped. At this point any continuation of wet grinding could damage the bone slice. It is carefully handled, and the process shifts from grinding to polishing to give bone slice a fine finishing. For polishing carborundum grade 600 and 1000 cc were used. The visibility of bone slice is very low. Traditionally at this point magnifying glass is used to make sure about the location of bone slice and polishing process goes without damage [40]. After preparation of few samples and practice, magnifying glass is not required. The polishing process starts with 600 cc grade. The carborundum paper section is dipped in distilled water. The process is similar to wet grinding. Polishing with 600 cc was done for 2e3 min while in the process few drops of distilled water was added on the bone slide with pipet. The sample is flipped after every 30e40 s with help of a paint brush carefully. This gave uniform polish to both sides of the bone slice. Fig. 2.14 shows the process of polishing with 600 cc. The grade was changed to 1000 cc and specimen was polished. After polishing for about 30e60 s, the specimen was washed in distilled water and placed on a clean glass slide. The slide was observed under microscope. If some sections were still thick and difficult to view, those sections of specimen were focused in polishing again and viewed again in microscope. Fig. 2.15 shows the final stages of polishing the bone slice and washing in distilled water.

Automated human cortical bone Haversian canal histomorphometric comparison system 39 (A)

(B)

Figure 2.14 (A) Polishing bone slice with grade 600 cc. (B) Flipping of the bone slice while polishing with paint brush.

(A)

(B)

Figure 2.15 (A) Bone slide in polishing final stages with grade 1000 cc. (B) Water bath of bone slice in distilled water to be viewed under microscope.

3.4 Glass slide mounting When thickness became uniformed and the desired microscopic view was obtained, the bone slice was cleaned by first by dipping in distilled water and then placing on a glass slide and gently spraying it with 50% diluted ethanol. The specimen was then placed on a clean and dry glass slide and 2e3 drops of xylene mounting medium was added on the bone slice with help of pipet. A cover slip was placed on the top of the slide such that one side of the cover slip touched first and the rest follow from that side. This way reduced the chances of trapped bubbles. The cover slip was place with the help of forceps. Fig. 2.16

40 Chapter 2 (A)

(B)

Figure 2.16 (A) Placing clean bone slice on new glass slide. (B) Addition on two to three drops of xylene mounting medium on top of bone slice.

demonstrates the process of placing bone slice on glass slide and addition of xylene mounting medium on bone slice. Usually a heavy metal or glass object less than the length of cover slip was placed on top of cover slip to let the access mounting medium spill out and reduce bubbles [39]. For this sample preparation process, coverslip box was place on top of the glass slide and left of 24 h. Fig. 2.17 demonstrates placing on coverslip on top of glass slide and pacing coverslip box on it for 24 h. After 24 h the weight was removed and the complete glass slide sample was cleaned with 50% diluted ethanol and dried with kitchen tissue. The sample slides were then labeled with names for identification. They were then carefully placed in sample slide boxes to prevent them from damaging. Fig. 2.18 shows the complete sample slide with label and its microscopic view under light emitting microscope with 4 times magnification.

4. Difficulties in sample preparation There were a number of difficulties faced in the preparation of bone slide sample from specimens collected. This section describes the problems faced and the steps done to overcome these problems.

4.1 Trapped air bubbles in the glass sample Trapped bubbles in the glass slide typically occurs due to the improper placement of coverslip. These bubbles trapped can be annoying while examining the sample under

Automated human cortical bone Haversian canal histomorphometric comparison system 41 (A)

(B)

(C)

Figure 2.17 (A) Placing coverslip on top of glass slide after adding xylene mounting medium with help of forceps. One side of the coverslip is touched first, and the rest follows. (B) and (C) Placing coverslip box as heavy object on top of the prepared slide to reduce bubbles and remove access xylene mounting medium. The prepared glass slide is placed on kitchen tissues so that the access xylene spilled is absorbed and does not affect glass slide visibility.

(A)

(B)

(C)

Figure 2.18 (A) Complete sample slide with labels. (B) and (C) Microscopic view of the sample with 4 times magnification.

42 Chapter 2 microscope. This has no relation to the bone slice preparation and finishing and is related to sample mounting. To avoid bubbles in samples coverslip should be place carefully from one side as described in Fig. 2.17A. Another reason of bubbles is the mounting medium. If the mounting medium is close to its expiry or not good quality, it may contain small bubbles inside it. To avoid bubbles from mounting medium it is necessary to have good quality mounting medium. when coverslip was not placed properly and bubbles were observed after placing, the coverslip was immediately removed, and the bone slice was placed on a new glass slide and the mounting process was started again with new coverslip.

4.2 Thick bone slice If the bone slice is not observed under microscope for its thickness before mounting, the sample may appear to be thick and could not provide good image for observation of microstructures. To avoid this problem the unmounted bone slice was placed on glass slide and observed under microscope for image quality. If the sample is found to be thick after the mounting on glass slide, the sample slide can be placed in xylene dissolvent solution which can dissolve the xylene mounting medium and release the bone slice. The bone slice has to go through grinding and polishing process again. If the bone slice has lost it whitish bone color it only needs to go through polishing process [39].

4.3 Uneven thickness Bone slices with uneven thickness provided good quality microscopic images from one region while other regions were observed less clear. This problem occurred when in grinding process the bone slice is not flipped or one region of the slice is grinded more the other. To avoid this problem the bone slice was placed on the glass slide before mounting and all regions were observed carefully for the visibility of microstructures. When a region of the bone slice appeared to be thick, the bone slice was polished again and the thick region was focused. After polishing the bone slice was observed again under microscope before mounting. Fig. 2.19 shows a comparison of two images taken from a single sample with uneven thickness.

4.4 Broken section This work was done using six long bones. Humerus and femur due to their greater cortical thickness were less prone to section breakage in the process of grinding and polishing. On the other hand, fibula provided very less cortical thickness and if not handled carefully the

Automated human cortical bone Haversian canal histomorphometric comparison system 43 (A)

(B)

Figure 2.19 Bone sample microscopic images of humerus obtained from 40 years old Malaysian female (A) Thick region of bone slice with less visibility of microstructures. (B) Region with observable microstructures under microscope.

bone slice broke into smaller sections. Tacking with broken bone slice section consumes more time than making new sample. Making bone sample slide of fibula specimen consumed greater time compared to other long bone specimens due to its delicacy. To avoid section breakage, the sample was handled carefully with delicate paint brush in the process of grinding and polishing. The grinding and polishing were also done gently.

4.5 Dirty specimen Following the above procedure produces clean and clear bone slice samples. However, in some cases the bone specimen was not completely cleaned. This made the bone slice to appear blur in microscope. The main reasons observed for this phenomenon was bone fat. In some cases, bone fat was not completely removed and while grinding and polishing process it got stick to the bone slice making the bone slice images blur under microscope. To remove the fat layer bone slice was given a water bath in distilled water in 90 C for 10 min and washed with ethanol. After water bath and cleaning with ethanol the sample was polished again with 600 and 1000 cc carborundum papers until it appeared clear under microscope. Other problems that may occur are due postmortem fungal invasion, recrystallized apatite of the bone matrix and soil and water minerals binding in bone matrix [41e43].

4.6 Fragile bone specimen Some of the bone specimen collected with thin cortical thickness were very fragile. The bone sectioning process of these specimen (mostly from fibula) was done more gently. To give strength to bone specimen while sectioning and grinding, few drops of xylene

44 Chapter 2

Figure 2.20 A broken fibula bone specimen while sectioning.

mounting medium was added on the sides and the specimen was kept for 24 h. Fig. 2.20 shown a broken fibula bone specimen while bone sectioning. If a specimen is broken and no other specimen is available, the pieces and can be joined together using cyanoacrylate glue (super glue). Creation of a bone slice form a fragile specimen takes twice the time compared to other specimens. Grinding and polishing process takes the most time. In some cases, the thickness of the bone slice couldn’t be achieved as of other specimens. The microscopic view of these samples was not as clear as other samples but further grinding and polishing could break the bone slice into small fragments. Fig. 2.21 shows a fragile sample and its microscopic view.

5. Image acquisition After the sample slides were created from bone specimen, their images were obtained to observe the microstructures. For image acquisition Nikon Eclipse Ts100 microscope was used. The microscope was set at phase contrast with four times magnification objective. DinoEye digital eyepiece microscope camera was used with the microscope to obtain images. The digital eyepiece was five-megapixel camera which was used DinoCapture 2.0 software. The software provided calibration of the camera with microscope four times magnification objective for measurements. In four times magnification images of the bone slice was obtained. The dimensions of each image were 1.28  0.96 mm which gives a total region area of 1.228 mm2. Eight images were taken from each sample. The regions were selected based on previous studies on Malaysian samples which observed more microstructure differences at these locations [13]. The selected regions are shown in Fig. 2.22. The bone slice was divided into four

Automated human cortical bone Haversian canal histomorphometric comparison system 45 (A)

(B)

(C)

Figure 2.21 (A) Fragile fibula bone slice sample. (B) and (C) Four times magnification microscopic view of fragile sample bone sample.

Figure 2.22 Regions selected for image acquisition. The bone slice was divided into four main sections and images were two images were obtained from each section of AM, PM, AL, and PL.

46 Chapter 2 sections anterior (A), posterior (P), median (M) and lateral (L). Four sections were selected located at the AM, AL, PL, and PM. From each section two image were obtained starting from the periosteum of the bone.

6. Microstructural parameter selection Six Haversian canal-based parameters were observed which are mean Haversian canal area (hcm), total area covered by Haversian canal (hca), mean Haversian canal radius (hcr), mean Haversian canal perimeter (hcp), total number of Haversian canal (hcn), and percentage area covered by Haversian canal in observed region (hcpar). Fig. 2.23 shows the Haversian canal parameters in microscopic bone image. The measurements of these parameters were performed using DinoCapture 2.0 software tool, which was calibrated with the microscope at four times magnification. The definition and measurement technique of microstructural parameters is described in the following sections.

6.1 Haversian canal number (hcn) Observed region is the microscopic image of a particular region of bone slice sample. The quantity and size of Haversian canals vary in the observed region. Total number of Haversian canal present in the observation region is referred as Haversian canal number (hcn).

6.2 Mean Haversian canal area (hcm) Mean Haversian canal area is the mean of area coved by Haversian canals in the observed region. This can be calculated using the following equation.

Figure 2.23 Microstructures selected for comparison.

Automated human cortical bone Haversian canal histomorphometric comparison system 47 hcm ¼

hcn X n¼1

 ðHaversian canal areaÞn hcn

6.3 Total Haversian canal area The total area covered by Haversian canals in a particular observation region is referred as total Haversian canal area (hca) and it can be calculated from the measurements using following formula. hcn X hca ¼ ðHaversian canal areaÞn n¼1

6.4 Mean Haversian canal radius Haversian canals in the human cortical bone does not have perfectly circular shape. To obtain the radius of particular Haversian canal, its radius was measured from the center from 8 to 10 points in the circumference manually. Fig. 2.24 shows the location of radius measurements. The radius of each Haversian canal (rad) is the mean of radii measured and can be expressed as 8 X rad ¼ rn =8 n¼1

The mean Haversian canal radius (hcr) is the mean of the radius of each Haversian canal present in the observed region. It can be expressed by the following equation. hcr ¼

hcn X n¼1

radn =hcn

Figure 2.24 Radius measurements of the Haversian canal in observation region.

48 Chapter 2

6.5 Mean Haversian canal perimeter The mean Haversian canal perimeter is the mean of perimeter of all the Haversian canals present in the observation area. It can be expressed in the following equation. hcp ¼

hcn X n¼1

 ðHaversian canal perimeterÞn hcn

6.6 Percentage area covered by Haversian canal (hcpar) The percentage area covered of Haversian canal is the percentage of area covered by the Haversian canal in total observed region. The area of observation region of each image is measured to be 1.228 mm2. The percentage area covered by Haversian canal can be calculated by using the following expression.   hcpar ¼ hca=observation region area  100 (observation region area ¼ 1.228 mm2).

7. Inclusion and exclusion criteria Most of the previous researches were mainly focused on osteon systems and their Haversian canal inclusion criteria was based on the intactness of the osteon system [3,4]. Since this research is mainly focused on the comparison of Haversian canal-based parameters, Haversian canals were selected irrespective of the shape and existence of osteon system around it. Haversian canals which were present with more than 50% of their area inside the observed region were selected. Volksmann’s canals were also present in some sample images which were connecting Haversian canals. In this scenario only Haversian canals were selected and measured based on manual observations.

8. Statistical tests Six Haversian canal-based parameters were compared in this section between two groups male and female. Normality of each parameter in the group was checked using ShapiroeWilk test (SW-test). The hypothesis obtained from the SW-test was: H0(SW-test): Two samples drawn from the population are normally distributed. Ha(SW-test): Two samples drawn from the population are not normally distributed. In case when null hypothesis H0 was accepted as the SW-test results, the parameters were considered normally distributed. When the null hypothesis was rejected and alternated hypothesis was accepted Ha, normality couldn’t be achieved in the samples.

Automated human cortical bone Haversian canal histomorphometric comparison system 49 For the parameters from the two group with normal data distribution, independent samples t-test was performed for the comparison. Data normality is the key assumption for accurate comparison in t-test. The hypothesis obtained from the t-test was: H0(t-test): m1 ¼ m2: There is no difference observed in the parameter of the two groups. If the population means of the observed parameter is equal, we accept the null hypothesis H0. On the other if the population means are not equal, we reject the null hypothesis and accept the alternate hypothesis Ha. The alternate hypothesis has three possible outcomes. Ha(t-test): m1 s m2: There population means are not equal in the observed parameter. Ha(t-test): m1 > m2: There population mean of group 1 is greater than group 2 in the observed parameter. Ha(t-test): m1 < m2: There population mean of group 1 is less than group 2 in the observed parameter. When the parameters are not normally distributed SW-test rejects the null hypothesis and t-test could not be applied. In this case Mann Whitney U-Test (Wilcoxon Rank Sum Test) was applied for parameters comparison. The hypothesis of this test was kept same as that of the t-test. The null hypothesis was accepted if no difference was observed in the two groups. H0(u-test): m1 ¼ m2: There is no difference observed in the parameter of the two groups. If the null hypothesis was rejected, the alternate hypothesis was set to have three cases. Ha(u-test): m1 s m2: There is difference in the observed parameter. Ha(u-test): m1 > m2: The observed parameter of group 1 is greater than group 2. Ha(u-test): m1 < m2: The observed parameter of group 1 is less than group 2.

9. Automated comparison system The automated comparison system is mainly designed to provide a singular platform to the forensic anthropologist and archeological experts for histological comparison of the selected parameters. The system prototype was designed in Matlab R2015a.

9.1 Comparison test selection The system was designed to automatically select the comparison test for the parameter in the groups based on the statistical tests described in Section 1.8. For automated test selection, normality of each parameter in both groups is tested using SW-test. If normality is achieved in parameter from both groups, t-test is selected for comparison. On the other hand, if data is not normally distributed in any or both the groups, nonparametric u-test is

50 Chapter 2

Figure 2.25 Flow chart of the automated normality and comparison tests on the selected parameters.

selected for comparison. The flowchart of the design of automated comparison test selection is demonstrated in Fig. 2.25.

10. Automated system design The user provides mean values of the measured parameters of all the samples in excel file. The automated system analyses all the parameters of samples-based requirement of user. Fig. 2.26 provides an overview of the automated system. The automated system provides user two main platforms of comparison. In the first platform two groups based on sex are made (male-female). In the second platform two groups are made based on the race. The system is divided to two tabs. The first tab provide platform for race comparison while the second tab is dedicated to sex comparison. The system provides user to select the races in the provided data for parameter

Automated human cortical bone Haversian canal histomorphometric comparison system 51

Figure 2.26 Automated cortical bone analyzer system overview.

comparison. Fig. 2.27 provides the graphical user interface of the histological Haversian canal parameters comparison platform for race. The selection of races for comparison can be made from the dropdown menu. The menu contains list of races in the provided sample data. Once selected the system automatically performs normality test of each parameter in both groups using SW-test and automatically suggests the applicable comparison test from the two parametric and nonparametric tests (t-test and u-test). After execution of the comparison test the system platform provides the user with numeric test results in table with significance and standard deviation of each compared parameter. The figure in the comparison platform provides user to view the cumulative fraction distribution of each parameter in comparison to view the parameter behavior among the groups. The cumulative fraction distribution function can be seen for the six compared parameters by using the buttons with parameters abbreviation as names. The sex comparison platform is similar to the race comparison platform without the option

Figure 2.27 Automated race histological Haversian canal parameter comparison platform.

52 Chapter 2

Figure 2.28 Comparison test execution section of the automated system for race and sex comparison.

of comparison group selection. Fig. 2.28 provides the test execution and result demonstration section of both race and sex comparison tabs.

11. Sex comparison without age groups For the comparison of between male and female groups, 30 male samples and 14 female samples were prepared. Eight images of each bone thin lice sample were taken. The measurement of parameters from eight images were taken and their mean values were selected for comparison. The descriptive statistics of the two groups with respect to observed parameters are given in Table 2.4. Table 2.4: Descriptive statistics of sex comparison. Parameter 2

hcm (mm ) hca (mm2) hcr (mm) hcp (mm) hcn (n/mm2) hcpar

Group Male Female Male Female Male Female Male Female Male Female Male Female

n 30 14 30 14 30 14 30 14 30 14 30 14

Mean 3189.3 4014.5 0.471 0.545 9.046 9.893 240.864 258.267 149.966 135.714 4.910 5.918

Automated human cortical bone Haversian canal histomorphometric comparison system 53 Table 2.5: ShapiroeWilk test on the parameters and selected comparison test for the parameters. Parameters hcm hca hcr hcp hcn hcpar

Groups

Sig

Male Female Male Female Male Female Male Female Male Female Male Female

0.081 0.206 0.0067 0.040 0.027 0.251 0.530 0.0679 0.224 0.107 0.004 0.0444

Stat 0.927 0.918 0.890 0.868 0.920 0.924 0.969 0.884 0.95 0.898 0.879 0.871

Hypothesis H0(SW-test) H0(SW-test) Ha(SW-test) Ha(SW-test) Ha(SW-test) H0(SW-test) H0(SW-test) H0(SW-test) H0(SW-test) H0(SW-test) Ha(SW-test) Ha(SW-test)

Comparison test selected t-test u-test u-test t-test t-test u-test

ShapiroeWilk test was applied on all the parameters in the two groups (male and female). The Haversian canal mean, hcm showed normal distribution in male and female samples with significance (P > .05). The comparison test selected for hcm was t-test. In case of hca, female sample showed normal distribution while male sample were not normally distributed (P > .05). The comparison test selected for this parameter was u-test. The parameters hcr and hcp showed normal distribution in both male and female groups and ttest was selected for their comparison. Male samples in hcpar and female samples in hcn did not show normal distribution (P > .05). U-test was selected for the comparison of these parameters (Table 2.5).

11.1 Sex comparison hcm The SW-test showed normal distribution in both male and female groups in hcm parameter. For comparison t-test was selected and the results are shown in Table 2.6. From the comparison it was observed that hcm is significantly different in both groups (P < .05). Female group showed greater hcm than males. Fig. 2.29 shows the comparison of cumulative fraction distribution of both groups. Table 2.6: Independent samples t-test on hcm for male and female group comparison. Sig t df sd

0.0337 2.1952 42 1161.27

54 Chapter 2

Figure 2.29 Cumulative fraction distribution of hcm in male and female group.

11.2 Sex comparison hca From the SW-test applied, it was observed that hca parameter is not normally distributed in the groups and t-test could not be applied. For comparison of hca, u-test was selected. The comparison test results how no difference in hca between males and female groups. Table 2.7 demonstrates the u-test results obtained from the comparison. For further illustration, cumulative fraction distribution of the two groups are shown in Fig. 2.30.

11.3 Sex comparison hcr The SW-test showed normal distribution in female hcr parameter but normality was not observed in male hcr parameter. U-test was selected for the comparison of hcr. Although it was observed that females from ages 40 and above showed greater Haversian canal size than males. With significance at (P < .05), the comparison test revealed no difference. The u-test applied for comparison is shown in Table 2.8. The difference of female hcr from male samples is demonstrated in the cumulative fraction distribution of compared parameter in Fig. 2.31. Table 2.7: Wilcoxon Rank Sum u-test on hca for male and female group comparison. Sig z Ranksum

0.504 0.667 648

Automated human cortical bone Haversian canal histomorphometric comparison system 55

Figure 2.30 Cumulative fraction distribution of hca in male and female group.

Table 2.8: Wilcoxon Rank Sum u-test on hca for male and female group comparison. Sig z Ranksum

0.0843 1.726 606

Figure 2.31 Cumulative fraction distribution of hcr in male and female group.

56 Chapter 2

11.4 Sex comparison hcp The hcp parameter in both male and female group showed normal distribution in SW-test and t-test was selected for comparison. From the comparison it was observed that hcp showed no difference in male and female samples (P < .05). The t-test result applied on hcp parameter is given in Table 2.9, while the cumulative fraction distribution of the parameter is demonstrated in Fig. 2.32.

11.5 Sex comparison hcn Using SW-test the normality of hcn in both groups were tested and it was observed that hcn is normally distributed in both groups. since normality was achieved, t-test was selected for comparison. The t-test showed no difference in the hcn parameter (P < .05). Table 2.10 shows the t-test performed for the comparison. It was observed that male samples with ages greater than 40 showed greater number compared to female samples. Since the comparison test are performed without age group, there it is not clear about the

Table 2.9: Independent samples t-test on hcp for male and female group comparison. Sig t df sd

0.142 1.496 42 35.94

Figure 2.32 Cumulative fraction distribution of hcp in male and female group.

Automated human cortical bone Haversian canal histomorphometric comparison system 57 Table 2.10: Independent samples t-test on hcn for male and female group comparison. Sig t df sd

0.162 1.423 42 30.936

Figure 2.33 Cumulative fraction distribution of hcn in male and female group.

difference in specified age groups. Fig. 2.33 gives the cumulative fraction distribution graph of hcn in male and female group.

11.6 Sex comparison hcpar The hcpar parameter was observed not normally distributed in both male and female group. The comparison test selected was U-test (Table 2.11). The test result shows no difference in hcpar with significance(P < .05). It was observed from cumulative fraction distribution graph (Fig. 2.34) and box-plot that female samples had greater hcpar after the age of 40 when compared to males. This requires comparison to be performed within specified age group to observe the difference.

11.7 Sex comparison discussion From the comparison of parameters in the male and female group using t-test and u-test, it was observed that hcm showed significant difference. Hcm was observed to be greater in the female group (P < .05) than male samples. Other parameters, hca, hcr, hcp, hcn, and

58 Chapter 2 Table 2.11: Wilcoxon Rank Sum u-test on hcpar for male and female group comparison. Sig z Ranksum

0.0843 1.726 606

Figure 2.34 Cumulative fraction distribution of hcpar in male and female group.

hcpar showed no significant difference. It was observed through the mean values that hcr and hcp were greater in female samples belonging to the fourth decade and above. Similarly, hcn was observed to be greater in male samples belonging to the fifth decade and above.

12. Race comparison without age groups Race comparison was performed in two main groups, Chinese and Indian in the acquired samples. Twenty-six Chinese, and 12 Indian samples were compared for six parameters, hcm, hca, hcr, hcp, hcn, and hcpar. These parameters were measured in eight images obtained from each sample and their mean value was obtained as the parameter value. Table 2.12 shows the descriptive statistics of the comparison between Chinese and Indian samples. SW-test was applied on the parameters of both groups (Chinese and Indian) to check normality of the parameters. It was observed that hcm was normally distributed in both groups (P > .05) in case of hca, in Indian samples normality was observed whereas in Chinese samples normality was not observed (P > .05). The parameters hcr and hcp

Automated human cortical bone Haversian canal histomorphometric comparison system 59 Table 2.12: Descriptive statistics of race comparison between Chinese and Indian samples. Parameter

Group

n

hcm (mm2)

Chinese Indian Chinese Indian Chinese Indian Chinese Indian Chinese Indian Chinese Chinese

26 12 26 12 26 12 26 12 26 12 26 12

hca (mm2) hcr (mm) hcp (mm) hcn (n/mm2) hcpar

Mean 3595.3 3301.1 0.531 0.448 9.495 9.175 250.142 241.033 147.88 135.75 5.546 4.576

Table 2.13: ShapiroeWilk Test on the parameters and selected comparison test for the parameters. Parameters hcm hca hcr hcp hcn hcpar

Groups Chinese Indian Chinese Indian Chinese Indian Chinese Indian Chinese Indian Chinese Indian

Sig

Stat

0.092 0.080 0.003 0.274 0.080 0.232 0.615 0.175 0.003 0.775 0.007 0.226

0.934 0.880 0.870 0.924 0.930 0.912 0.969 0.908 0.861 0.959 0.885 0.917

Hypothesis H0(SW-test) H0(SW-test) Ha(SW-test) H0(SW-test) H0(SW-test) H0(SW-test) H0(SW-test) H0(SW-test) Ha(SW-test) H0(SW-test) Ha(SW-test) H0(SW-test)

Comparison test selected t-test u-test t-test t-test u-test u-test

showed normal distribution in both groups (P > .05). Chinese group parameters hcn and hcpar didn’t show normal distribution, while these parameters show normal distribution in Indian samples (P > .05). Detailed SW-test on all parameters of Chinese and Indian groups is given in Table 2.13.

12.1 Race comparison hcm The SW-test on hcm revealed normal distribution of data in both groups. For comparison t-test was selected. No difference was observed within both groups (P < .05). The t-test applied on is demonstrated in Table 2.14. It was observed in cumulative fraction distribution graph that hcm in Indian samples were greater than Chinese samples in the

60 Chapter 2 Table 2.14: Independent samples t-test on hcm for Chinese and Indian group comparison. Sig t df sd

0.501 0.678 36 1242.05

Figure 2.35 Cumulative fraction distribution of hcm in Chinese and Indian group.

second and third decade (Fig. 2.35). Age graded comparison test is required to further investigate and understand the behavior of hcm in the groups.

12.2 Race comparison hca SW-test revealed that the hca parameter was not normally distributed in Chinese samples. Non-parametric comparison test (u-test) was selected for comparison. The u-test revealed no difference in hca parameter in the selected groups. Table 2.15 demonstrates the result of u-test on hca parameter. For further illustration cumulative fraction distribution graph of hca in both groups is given in Fig. 2.36. From the parameter mean values it was observed Table 2.15: Wilcoxon Rank Sum u-test on hca for Chinese and Indian group comparison. Sig z Ranksum

0.499 0.675 529

Automated human cortical bone Haversian canal histomorphometric comparison system 61

Figure 2.36 Cumulative fraction distribution of hca in Chinese and Indian group.

that after the fifth decade, Chinese samples showed relatively greater hca values than Indian samples.

12.3 Race comparison hcr The hcr parameter was observed to be normally distributed in both groups using SW-test. T-test was applied for comparison and it was observed that there was no difference in hcr parameter in Chinese and Indian groups (P < .05) (Table 2.16). The cumulative fraction distribution graph of hcr parameter is demonstrated in Fig. 2.37. From the parameter means it was also observed that Chinese samples after the fifth decade showed greater hcr values compared to Indian samples.

12.4 Race comparison hcp SW-test was applied on hcp parameter in both groups and it was observed that the parameter is normally distributed (P > .05). Comparison was performed between the group using t-test. The t-test revealed no difference in the hcp parameter with significance Table 2.16: Independent samples t-test on hcr for Chinese and Indian group comparison. Sig t df sd

0.542 0.614 36 1.496

62 Chapter 2

Figure 2.37 Cumulative fraction distribution of hcr in Chinese and Indian group. Table 2.17: Independent samples t-test on hcp for Chinese and Indian group comparison. Sig t df sd

0.495 0.688 36 37.93

(P < .05). Table 2.17 gives the t-test result with the significance, t-vale and standard deviation in the parameter. From the measured values of hcp parameter, it was observed that Indian samples from the second and third decade were greater than Chinese samples. The cumulative fraction distribution of the parameter hcp is demonstrated in Fig. 2.38.

12.5 Race comparison hcn Normality was not observed in the hcn parameter in Chinese group by using SW-test (P > .05). U-test was selected for the comparison of hcn parameter. Comparison test revealed no difference in the parameter (P < .05). The comparison test results are given in Table 2.18 and the cumulative fraction distribution graph of hcn is demonstrated in Fig. 2.39.

12.6 Race comparison hcpar The hcpar parameter was observed as not normally distributed using SW-test. Nonparametric test (u-test) was selected for the comparison of hcpar in Chinese and Indian

Automated human cortical bone Haversian canal histomorphometric comparison system 63

Figure 2.38 Cumulative fraction distribution of hcp in Chinese and Indian group.

Table 2.18: Wilcoxon Rank Sum u-test on hcn for Chinese and Indian group comparison. Sig z Ranksum

0.479 0.706 530

Figure 2.39 Cumulative fraction distribution of hcn in Chinese and Indian group.

64 Chapter 2 Table 2.19: Wilcoxon Rank Sum u-test on hcpar for Chinese and Indian group comparison. Sig z Ranksum

0.465 0.738 531

group. U-test revealed no difference in parameter. The u-test results are given in Table 2.19 and the cumulative fraction distribution graph is shown in Fig. 2.40. In the fifth decade its was observed that Chinese samples showed greater hcpar than Indian samples but overall the decades this difference was not observed.

12.7 Race comparison discussion The comparison was performed between the two groups (Chinese and Indian) using t-test and u-test. The result showed that there was no significant difference (P < .05) obtained in any of the six compared parameters (hcm, hca, hcr, hcp, hcn, hcpar). However, it was observed in the mean values that Indian samples in the second and third decade showed greater hcm, hcr, and hcp parameter compared to Chinese samples. Chinese samples in the fourth and fifth decade showed greater hca, hcr, and hcpar. To obtain the exact behavior of Haversian canal within the race groups, a detailed age graded parameter comparison is required.

Figure 2.40 Cumulative fraction distribution of hcpar in Chinese and Indian group.

Automated human cortical bone Haversian canal histomorphometric comparison system 65

13. Conclusion The research has discussed in detail human cortical bone slice sample preparation and the difficulties faced in the process. A compilation of the previous work done on histological analysis of human cortical bone provided in this research can be referred for future studies and population comparison. The main objective of the research was to provide population comparison and histological variance of Haversian canal parameters in human cortical bone. The race comparison performed among Malaysian groups (Chinese-Indian) showed no difference in the parameters while in sex comparison mean Haversian canal area (hcm) was significantly greater in females compared to males (0.05). Although the results are satisfactory, it is advised to consider these results as the building block and further research is required to justify and expand the research outcomes. The automated system presented in this research provides a dedicated platform for the forensic anthropologists to perform comparison among sex and race of multiple population data. Further research is currently being conducted to automate the histological parameter measurement process which consumes maximum time of the experts. Automation of the parameter measurement is currently performed using microradiography but this system has its limitation in high cost, availability and specific bone slice thickness requirement.

References [1] S. Black, A Tour of Mile Canyon, The University of Texas at Austin and College of Liberal Arts, 2001. [2] I. Khan, M.M.A. Jamil, T.N.T. Ibrahim, F.M. Nor, Analysis of age-related changes in Haversian canal using image processing techniques, in: 2016 6th IEEE International Conference on Control System, Computing and Engineering (ICCSCE), 2016, pp. 169e172. [3] H. Abdullah, F. Nor, M.M. Abdul Jamil, Human bone histomorphological pattern differences between genders: a review, in: International Conference for Innovation in Biomedical Engineering and Life Sciences, vol. 56, 2015. [4] I. Khan, M.M.A. Jamil, T.N.T. Ibrahim, F.M. Nor, Automated human age estimation at death via bone microstructures, in: 2016 6th IEEE International Conference on Control System, Computing and Engineering (ICCSCE), 2016, pp. 580e583. [5] S.M. Schoenbuchner, J.M. Pettifor, S.A. Norris, L.K. Micklesfield, A. Prentice, K.A. Ward, Ethnic differences in peripheral skeletal development among urban South African adolescents: a ten-year longitudinal pQCT study, Journal of Bone and Mineral Research 32 (December 2017) 2355e2366. [6] C.D.L. Thomas, S.A. Feik, J.G. Clement, Regional variation of intracortical porosity in the midshaft of the human femur: age and sex differences, Journal of Anatomy 206 (2005) 115e125. [7] S. Swee-Hock, The Population of Malaysia, second ed., Institute of Southeast Asian Studies, 2015. [8] J.P. Malaysia, Population Quick Info, 2017. [9] D. o. s. Malaysia, Ethnic Composition, Current Population Estimates, Malaysia, 2014e2016, Department of Statistics Malaysia, Official Portal, 2014. [10] A. Ibrahim, A. Alias, F.M. Nor, M. Swarhib, S.N. Abu Bakar, S. Das, Study of sexual dimorphism of Malaysian crania: an important step in identification of the skeletal remains, Anatomy and Cell Biology 50 (June 2017) 86e92.

66 Chapter 2 [11] A. Alias, A. Ibrahim, S.N. Abu Bakar, M.S. Shafie, S. Das, F. Nor, Morphometric and Morphological Study of Mental Foramen in the Malaysian Population: Anatomy and Forensic Implications, vol. 16, 2017. [12] S.N. Abu Bakar, A. Aspalilah, I. AbdelNasser, A. Nurliza, M.J. Hairuliza, M. Swarhib, et al., Stature estimation from lower limb anthropometry using linear regression analysis: a study on the Malaysian population, Clinica Terapeutica 168 (MarcheApril 2017) e84ee87. [13] F.M. Nor, R.F. Pastor, H. Schutkowski, Age at death estimation from bone histology in Malaysian males, Medicine, Science & the Law 54 (October 2014) 203e208. [14] F. Nor, R.F. Pastor, H. Schutkowski, Histological study to differentiate between human and non-human long bone, International Medical Journal 22 (2015). [15] M.M.A. Jamil, I. Khan, H. Abdullah, F.M. Nor, Microscopic analysis of bone microstructures with increasing age in Malaysian females 10 (2018) 12. [16] A. Hadi, J. Muhammad Mahadi Abdul, N. Faridah Mohd, Histomorphometric variance of haversian canal in cortical bone of Malaysian ethnic groups, Journal of Physics: Conference Series 1019 (2018) 012009. [17] A. Hadi, J. Muhammad Mahadi Abdul, A. Radzi, N. Faridah Mohd, Bone histology: a key for human sex determination after death, Journal of Physics: Conference Series 1019 (2018) 012010. [18] M.M.A.J. Hadi Abdullah, F.M. Nor, Sex based histological comparison of Haversian canal in cortical bones of Malaysian citizens, Journal of Fundamental and Applied Sciences 10 (2018). [19] K. Ijaz, J. Muhammad Mahadi Bin Abdul, N. Faridah Mohd, Age regression for Malaysian males using cortical bone Histomorphometry, Journal of Physics: Conference Series 1019 (2018) 012011. [20] I. Khan, F. Mohd Nor, M.M. Abdul Jamil, A survey of human age estimation techniques from bone microstructures, in: F. Ibrahim, J. Usman, M.S. Mohktar, M.Y. Ahmad (Eds.), International Conference for Innovation in Biomedical Engineering and Life Sciences : ICIBEL2015, 6e8 December 2015, Putrajaya, Malaysia, Springer Singapore, Singapore, 2016, pp. 203e207. [21] T.D. White, M.T. Black, P.A. Folkens, Skull: cranium and mandible, in: Human Osteology, third ed., Academic Press, San Diego, 2012, pp. 43e100 (Chapter 4). [22] F.M. Nor, A Comparative Microscopic Study of Human and Non-human Long Bone Histology, Ph. D, Department of Archaeological Sciences, University of Bradford, 2010. [23] H. Abdullah, M.M.A. Jamil, F.M. Nor, Automated haversian canal detection for histological sex determination, in: 2017 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE), 2017, pp. 69e74. [24] I. Khan, M.M.A. Jamil, F.M. Nor, Evaluation and reliability of bone histological age estimation methods, Journal of Fundamental and Applied Sciences 9 (2017) 18. [25] E.R. Kerley, The microscopic determination of age in human bone, American Journal of Physical Anthropology 23 (1965) 149e163. [26] I.J. Singh, D.L. Gunberg, Estimation of age at death in human males from quantitative histology of bone fragments, American Journal of Physical Anthropology 33 (November 1970) 373e381. [27] D.D. Thompson, The core technique in the determination of age at death of skeletons, Journal of Forensic Sciences 24 (October 1979) 902e915. [28] D.D. Thompson, M. Gunness-Hey, Bone mineral-osteon analysis of Yupik-inupiaq skeletons, American Journal of Physical Anthropology 55 (1981) 1e7. [29] D.B. Burr, C.B. Ruff, D.D. Thompson, Patterns of skeletal histologic change through time: comparison of an archaic native American population with modern populations, The Anatomical Record 226 (March 1990) 307e313. [30] M.F. Ericksen, Histologic estimation of age at death using the anterior cortex of the femur, American Journal of Physical Anthropology 84 (February 1991) 171e179. [31] D.M. Mulhern, D.P. Van Gerven, Patterns of femoral bone remodeling dynamics in a Medieval Nubian population, American Journal of Physical Anthropology 104 (September 1997) 133e146.

Automated human cortical bone Haversian canal histomorphometric comparison system 67 [32] K.L. Bell, N. Loveridge, J. Reeve, C.D. Thomas, S.A. Feik, J.G. Clement, Super-osteons (remodeling clusters) in the cortex of the femoral shaft: influence of age and gender, The Anatomical Record 264 (December 1, 2001) 378e386. [33] H.M. Britz, C.D. Thomas, J.G. Clement, D.M. Cooper, The relation of femoral osteon geometry to age, sex, height and weight, Bone 45 (July 2009) 77e83. [34] M.-T.J. Cosgriff-Hernandez, Histomorphometric Estimation of Age at Death Using the Femoral Cortex: A Modification of Established Methods, The Ohio State University, 2012. [35] T.D. White, M.T. Black, P.A. Folkens, Arm: humerus, radius, and ulna, in: Human Osteology, third ed., Academic Press, San Diego, 2012, pp. 175e198 (Chapter 9). [36] T.D. White, M.T. Black, P.A. Folkens, Hand: carpals, metacarpals, and phalanges, in: Human Osteology, third ed., Academic Press, San Diego, 2012, pp. 199e218 (Chapter 10). [37] T.D. White, M.T. Black, P.A. Folkens, Leg: femur, patella, tibia, and fibula, in: Human Osteology, third ed., Academic Press, San Diego, 2012, pp. 241e270 (Chapter 12). [38] T.D. White, M.T. Black, P.A. Folkens, Foot: tarsals, metatarsals, and phalanges, in: Human Osteology, third ed., Academic Press, San Diego, 2012, pp. 271e294 (Chapter 13). [39] George J.R. Maat, Robert P.M. Van Den Bos, Mj (Job) Aarents, Manual preparation of ground sections for the microscopy of natural bone tissue: update and modification of Frost’s ‘rapid manual method’, International Journal of Osteoarchaeology 11 (2001) 366e374. [40] H.H. de Boer, M.J. Aarents, G.J.R. Maat, Staining ground sections of natural dry bone tissue for microscopy, International Journal of Osteoarchaeology 22 (2012) 379e386. [41] C.J. Hackett, Microscopical focal destruction (tunnels) in exhumed human bones, Medicine, Science & the Law 21 (October 1981) 243e265. [42] B. Herrmann, H. Newesely, Dekompositionsvorga¨nge des Knochens unter langer Liegezeit 1. Die mineralische Phase, Anthropologischer Anzeiger 40 (1982) 19e31. [43] A.K. Behrensmeyer, Taphonomic and ecologic information from bone weathering, Paleobiology 4 (1978) 150e162.

CHAPTER 3

Biomedical instrument and automation: automatic instrumentation in biomedical engineering R.J. Hemalatha, R. Chandrasekaran, T.R. Thamizhvani, A. Josephin Arockia Dhivya, K. Sangeethapriya, A. Keerthana, G. Srividhya Department of Biomedical Engineering, Vels Institute of Science, Technology and Advanced studies, (Deemed to be University), Chennai, India

Chapter Outline 1. Introduction 70 2. Biomedical instrumentation 72 3. Automation in the field of biomedical instrumentation 3.1 Automation in medical instruments

4. Automation in telerobotic surgeries

78

80

80

4.1 Origin of surgical robots 81

5. Types of robotic surgeries

81

5.1 Type-1 supervisorydcontrolled surgery systems 81 5.2 Type-2 shared-control robotic surgery systems 82 5.3 Type-3 tele surgical robotic surgery system 82 5.3.1 da Vinci Surgical System 82 5.3.2 ZEUS robotic surgical system (ZRSS) 82 5.3.3 Automated endoscopic system for optimal positioning (AESOP) robotic surgical system

6. Applications

83

6.1 PROS and CONS of surgical robots 6.1.1 PROS 83 6.1.2 CONS 84 6.2 The future of surgical robots 84

83

7. Automatic wireless sensor networking in biomedical instrumentation 8. Biomedical applications of wireless sensor networking 85 8.1 IEEE 802.15.4 86 8.2 Open system interconnect layered architecture

9. Network topology 87 10. Bluetooth communication

86

87

10.1 Bluetooth modules used for biomedical applications

88

Handbook of Data Science Approaches for Biomedical Engineering. https://doi.org/10.1016/B978-0-12-818318-2.00003-9 Copyright © 2020 Elsevier Inc. All rights reserved.

69

84

82

70 Chapter 3 11. Sensing technologies 11.1 11.2 11.3 11.4

88

Invasive biosensors for WSN 89 Noninvasive bio sensors for WSN 89 Respiration rate sensor 89 RF and antenna communication 90

12. Selecting RF transceivers

90

12.1 Specifications 90 12.2 Safety issues 91

13. Recent advancements and applications in biomedical instrumentation 13.1 13.2 13.3 13.4 13.5 13.6 13.7 13.8

Biomedical instrumentation Biomedical instrumentation Biomedical instrumentation Biomedical instrumentation Biomedical instrumentation Biomedical instrumentation Biomedical instrumentation Applications of automation

in in in in in in in in

91

medical imaging 92 medical devices 93 tissue engineering 93 implants and bionics 94 clinical engineering 95 neural engineering 95 rehabilitation engineering 96 biomedical instrumentation 96

14. Conclusion 97 References 98

1. Introduction Biomedical Engineering defines the advanced knowledge and innovations in biology, medicine, and engineering. These fields of engineering enhance human health with the help of cross-disciplinary activities. The activities associate the engineering fields with clinical practices and biomedical sciences. Biomedical Engineering helps in development of innovative ideas by understanding the nature of the living system with the help of engineering techniques. The design, development, and application of algorithms, processes, and systems by interfacing engineering sciences with medical practices are to improve the health care sector. Healthcare is related with engineering designs and fields to develop and establish a well organized health society. Biomedical engineering is an interdisciplinary sector which involves different fields. One among them is biomedical instrumentation that deals with equipment have used for the betterment of the health sector [1]. Biomedical Instrumentation deals with the study of instruments used for recording of bio potentials of different types. This field of instrumentation also describes the internal circuits of the equipment specially designed for diagnosis and treatment. Circuit designing and working of all instruments related with the health care sector is studied under biomedical instrumentation. Advancements in medical instruments have paved path for early diagnosis, detection and treatment of various diseases or disorders. Innovations and developments in this sector developed a society with advanced health facilities. Automation in biomedical instrumentation enabled the physicians to diagnose the

Biomedical instrument and automation 71 abnormalities at a single press of a button. Instruments are designed in such a way that they are fully automated and easy to handle even by the common people [2]. Best example to be defined for this kind of automation is the glucometer which diagnosis the sugar level in the blood. Glucometer has been designed completely automatic that with a single prick the level of glucose in the blood is defined along with the medical conditions of the sample subject. Further advancements in this field of instrumentation have made smartphones as a diagnostic tool for diseases or disorders [3]. Automation in biomedical instrumentation explains the automatic designing of the medical instruments for the identification and treatment of abnormalities. Automation explains the use of control system in the instruments and automatic operation of the instruments. Automation of medical instruments helps to perform all kinds of activities without time consumption. Clinical laboratories with automated instruments are capable of performing wide variety of tests for large number of samples within limited time span. Automation in the laboratories emphasize the degree of simplification for the usage of the instruments by anyone after proper training. Automation has been introduced in all small hospitals and even in clinics for easy diagnosis and effective patient care. The motivation or idea for the development of the automation in the field of medical instrumentation came into frame because of the pressure built on the experts for diagnosis and treatment. This is mainly to develop a well-advanced health care society for the people. Innovations in the field of automation especially in the development of the medical instruments have enabled a better standard of patient care. Innovations in medical instruments has helped us to experience a new era of medical field which enhances the life expectancy, quality of life, diagnosis and treatment. Health industry developed with a high efficiency and cost effectiveness. Automatic innovations in this field of engineering relates with the control mechanism of the instruments designed mainly for diagnosis and treatment. Automation has influenced to such an extent that even the bio potentials, medical images, and bio signals can be transmitted and received using telemedicine. Robotic surgeries completely automated is designed and being studied to deduce the failure rate of the complicated operations, specifically neurosurgeries. Instrumentation used in operation theaters is automatically to illustrate the region, position, angle of incision, and cutting strategy of the abnormality for which the surgery is performed. Clinicians make use of these automated equipment to determine the state or nature of the abnormality. Advancements in the diagnostic equipment describe that determination of any sort of abnormality or even cancerous cells in the body has become more efficient. Effective training and use of the instrument has helped in diagnosis of wide range of disorders. Hereditary disorders can also be diagnosed with the help of the advanced instruments from the womb of the mother. Medical instruments are specially designed and structured for the

72 Chapter 3 treatment of the different variety disorders or diseases. Advancement in laser technology has configured special surgical procedures in the medical field for treatment. Internet of Things (IoT) and Information Technology developed a completely automatic environment for the medical instruments [4]. Diagnostic tools for various disorders are defined with the automatic internal circuits. Automatic instruments designed for the recording of bio potentials like ECG, EEG, EMG, EOG, ERG, EGG, and VEP sensed a way for diagnosis of disorders. By analyzing these analog signals, the nature and condition of the particular organ of the body is studied. Analysis of bio signals are designed along with the recording equipment for effective study. Imaging techniques and instruments capture images based on different mechanism which are used for the diagnosis of a disease or abnormality. X-ray, CT, MRI, Ultrasound, PET, SPECT, f-MRI are certain types of imaging modalities that produce different sectional images of the regions of the body considered for the diagnosis. Imaging systems help in the diagnosis and also assist physicians during surgeries. All these instruments or equipment with different automatic mechanism are used in the different sectors of health care and medicine. In this chapter, mechanism, design, structure, innovations, advancements, and automation in the medical equipment used in various fields are analyzed and discussed.

2. Biomedical instrumentation Instrumentation engineering is defined as the stream which is mainly focused on measuring instruments. They are used in designing equipment, electrical instruments, etc. Instruments are typically used in the field of automotive, chemical or manufacturing industries. The main goal of instrumentation system is optimization, safety, accuracy, productive, and being stable. Instrumentation principles have been applied to biomedical equipment and many types of bio sensors are used in biomedical instrumentation. Selection is mainly based on cost, reliability and good environmental factors. Biomedical engineers are used to record the human signals and it is used to record signals. Instrumentation technologists are used to troubleshoot the instruments, whereas biomedical technologists troubleshoot the medical equipment and maintaining the instruments [5]. Instrument is a term which is used to measure and record the human signals. Instrumentation refers to a group of devices. Here sensor plays a vital role in the mechanism. Biomedical engineering is defined as the application of medicine and technology together for health care purposes. Instruments in this field help in monitoring patients, diagnosing, and in therapy process. Many biomedical research is based upon prosthesis, diagnostics, and therapeutics.

Biomedical instrument and automation 73 Biomedical instrumentation is a subbranch of the biomedical engineering field, which is mainly focused on how electronics can measure the physical parameters and it improves medical care. Medical Instrumentation involves in designing new devices which solves health sector related problems by adapting recent advancements in medicine and engineering. Biomedical engineers are the ones who design the medical device or instruments. Many types of instruments ranging from X-ray, magnetic resonance imaging (MRI) to small devices such as pacemakers, drug delivery system, cochlear implants, and infusion pumps help in diagnostic and therapeutic medical devices. It is an innovative product which helps to reduce the cost and it improves the technology. Recent trending technologies are in the field of wound dressing, tissue engineering, etc. Biomedical optics is a field which involves the interaction of human biological tissue and light, then the result will be processed for imaging purposes [6]. Engineers design instruments and devices, and also they carry out research and solve the problems which include acquiring innovative understanding of living organisms and applying engineering skills on application. Implementing new procedures and algorithms helps in improving health care. Basically, in instrumentation, the sensor plays a vital role in the instrumentation system. It helps to convert the information from one form to another form. The diaphragm is an element in the sensor, which helps to convert the term pressure into displacement. The acquired signal will be processed, filtered, and monitored. Measurand is a basic system which helps to measure the physical parameters of a human. Simple examples for these instruments can be blood pressure, ECG, and so on. Instruments used in signal conditioning are mainly defined to amplify, condition, and filter the signal. The signals will be converted into digital form with the help of microcomputer. Then the output is described in the display unit. Output is represented in the form of values, graphs, or tables. There are various technologies implemented to study and analyze the human body system. Cardiovascular technology defines all the medical instruments, implanted devices associated with cardiovascular system. Neurological techniques deals with the devices, drugs related to brain system and its nerves. Orthopedic technology states about all the implanted devices related to skeletal systems. Cancer technology relates to all the devices, drugs related to cancer diagnosis and treatment [7]. A medical device is used for diagnosing a disease, treatment, and prevention of disease. Examples for these instruments are pacemakers, infusion pumps, contact lens, dental implants, etc. Implantable pacemaker design is shown in Fig. 3.1. Implant is defined as a type of medical tool, which is used to replace a missing function. Classification of biomedical instruments includes measuring the required physical parameter such as temperature, pressure, or flow. The next step includes whether the instrument is resistive or capacitive. Measurement techniques are available for cardiovascular, neuromuscular, and endocrine systems.

74 Chapter 3

Figure 3.1 Implantable pacemaker. Courtesy: http://www.heartsurgeons.com/procedures7.html.

Biomedical instrumentation has several advantages over other fields. It is defined as embedding different modules in a system. This instrumentation is used in the section of telemedicine. Embedding system will face challenges in future regarding health care. Health care applications are used widely in imaging techniques. Bioengineering is defined as interdisciplinary field where all streams of engineering are involved. Embedding modules with artificial intelligence is trending nowadays. It supports both wired and wireless sensors. Embedded systems acquire high performance and high accuracy. Biomedical engineering involves finding out more solutions for problems related to medical health care. Biomechanics is also a field of bioengineering which is used to study in detail about the structure of the biological organs [8,9]. There are many types of biomedical equipment. Medical imaging techniques like ultrasound, MRI, and CT Scans. Life supporting equipment are used to maintain and monitor the patient’s body condition. These include equipment’s like ventilators, incubators, heart lung machines, and hemodialysis machines. Parameters of human body can be measured using appropriate machines. ECG is used to measure the heart rate signals. EEG is used to pick up the electrical activity of the brain. Blood pressure apparatus is used to measure the blood pressure of the patient. A biomedical engineer is used in the health care section. They are mainly responsible for working of a medical instrument. They act as an intermediate between the physicians and the medical equipment [10].

Biomedical instrument and automation 75 Safety plays a vital role in biomedical instrumentation. It gradually decreases the ill people and it reduces mortality rate. The major causes of accidents occur due to improper training of staffs, improper maintaining of medical equipments, malfunctioning of medical equipments. In early years, processors and controller was monitored by a manual operator which is used to measure the pressure, temperature, and flows. A biosensor is used to measure a biological response and it is converted into an electrical signal. It involves in designing and developing new analytical devices to solve health related issues. Biomedical instruments ranges from ultrasound imaging machine to small implantable pacemakers. It involves combination of engineering and biology to solve health issues. They are mainly used to determine the concentration of the biological substance. Bioengineering is a new evolutionary product that enhances the technology and is cost effective. Recent progress is in the field of the biomaterials and biomedical applications are also helpful in the field of drug delivery systems [11]. Biomedical engineering is classified into tissue engineering where it plays a vital role in bodily fluids and tissues. They are currently working in the field of designing organs. Recent discoveries include artificial urinary bladders. Tissue engineering also includes the process of manipulating genes. Neural engineering is a technique where the neural system will be enhanced. This technique is used to solve problems and analyze the difference between neural tissue and nonliving tissues. Neural engineers work intensely to solve designs related to neural tissues. Cognition and trending research in neural fields enables us to study and analyze the pattern of training and functioning of neural structures for a particular source of action. Artificial intelligence in the field of medicine has opened sources to many noninvasive or minimally invasive techniques for both diagnosis and treatment. Classification of biomedical instruments includes the study of biomedical equipment. The measured quality will be sensed such as temperature, pressure, and flow. The main advantage of classification is it helps to measure the quantities easily. They are mainly useful for wired and wireless sensors and actuators. Security systems have also extensively enhanced to save patients data. It is specially designed to keep the patients data securely. Advancements in security system for protection of data led to the use of biometrics for storage of confidential details. Biomedical applications have been extensively used in biotechnological applications. It is mainly used in wound dressings and suture processes. Coatings are very important for blocking the antifouling effect. It has also good effects on collagen and gelatin that is extensively used in biomaterials for drug delivery systems. Maintenance plays an important role in drug delivery systems because target systems are marked for delivering the medicine. Biomaterials specially produced for implants are designed with high biocompatibility rate. Research oriented field of study in biomedical engineering.

76 Chapter 3 Software-based designing is performed and printed out to form volume-based structures in different materials. Not only designing of implants but also instruments are designed for fixing and positioning the implants at the exact location [12]. Especially in implant surgeries, the location or region where the implant to be placed is analyzed through imaging techniques and these images help in to develop a computerized layout for fixation of the implants exactly at the correct region. Medical imaging technique also comes under medical instrumentation. The physicians will find it easy to view the size and location of the diseased organ. Medical or biomedical imaging is a major sector related of medical devices used for imaging. They are widely used in the field of radiology. Instrumentation is also involved in implantation such as it is used to replace the missing organ. The material which is used to replace the organs is made up of silicone rubber and titanium. Implants include cochlear implants and pacemaker. Bionics is also a replacement of a biological structure. Bionics is mainly used for organ replacement. Biomedical instrumentation is used for replacement of human body. They work with the physicians for performing artificial body parts [13]. Clinical engineering is a special field in biomedical engineering which deals with the use of medical equipment and technologies for clinical applications. Clinical engineers mainly focus on supervising biomedical equipments and servicing the medical equipments. For example, measuring of exact heart rate, saturation level of oxygen, blood pressure and so on for the athletes and sports person is made possible using simple devices. It is fashionable and invisible. These sensors can be embedded and integrated in clothes. Power supply is needed for energy consumption [14,15]. Bio sensors specially designed to analyze any kind of electrical signals or energy derived from the biological cells or bio chemical particles. Protein, amino acid, DNA structures, Antibody, Enzyme chemical composition, Ionic substances in the cell structures and all other internal substances can be analyzed and studied using the bio sensors which shown in Fig. 3.2. Bio sensors study the energy released by the biological substances and illustrate the results that can be interpreted for diagnosis of medical conditions [11]. Biomechanics is a special field of biomedical engineering which deals with study of usage of mechanics by the living organism. Instrumentation in this field is related to sensors for calibrating speed of the patient, sensing motor ability of the individual postphysiotherapy treatment. Gait analysis and stabilizing the motor activities are performed with these instruments designed. For diagnosis of the motor functions of the individuals, pre- and posttreatment is obtained with the help of highly configured instruments. These instruments are designed to record the motor activities and analyze them automatically. Advanced signal processing is used in biomedical instrumentation. Fourier series signals are used here. These types of transforms are used extensively in neuroimaging technique.

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Figure 3.2 Functioning of bio sensor for data analysis. Courtesy: https://www.mdpi.com/2079-6374/5/3/537/htm.

In biomedical measurements, advanced machine learning algorithms are widely studied. They are used in separating and defining the nature of the bio signals. Analog signals are recorded from the human source for diagnosis and monitoring purposes. Numerous bio signals can be derived from the human body. Each organ of the body produces electrical signals defined as bio signals. Automatic equipment are specially designed to record these low amplitude signals. Different circuits are used to obtain efficient signal [16]. Home based rehabilitation has also been widely evolved in recent years of biomedical instrumentation. They have good accuracy and reliability. They are used to measure and analyze the patient’s performance. It has been dependent on physiological and environmental factors. Instrumentation in biomedical engineering combines both the knowledge of engineering and biology for developing health care. They are called for mainly to develop instruments and devices to acquire information to solve all the new problems. It is an interdisciplinary domain where engineers design several researchers explore from nanomedicine to robotics in telesurgery. It is the application of electronics and measurement techniques to design and fabricate the medical devices. They are used in diagnosis and treatment of a disease. Computerized technologies play a vital role in bio instrumentation; here we use microprocessors to do all the large number of small tasks in a single purpose instrument. It is a simple method to implement the technique. Biomedical field is a challenging field, where all the fields are involved in it. It is used to educate the future researches to know about the government regulations regarding safety standards and efficiency. Measurement is an important factor which is used in medical, biomedical and health care fields. Because it forms the medical and evaluation, it is used extensively in the field of biomedical instrumentation. Measurement is very important

78 Chapter 3 because it is the ultimate first process in biomedical instrumentation. If measurement is not proper, it results in the wrong decision of the patient. Confidence is an important factor in biomedical instrumentation. Doctors and physicians should believe and trust the results recorded by the instrument. If the instrument results are not correct it results in dissatisfaction of patient. Sometimes it results in loss of a patient’s life. So measuring the data is an important factor in hospital health care [17]. Instrumentation and measurements is used for continuous monitoring, imaging of patient parameters. Advancements in today’s worldinclude touch screen, voice, and gesture recognition. It has created a boom in the health care industry. The main aspects of biomedical instruments include: • • • •

Accuracy Reliability Portable Calibration

Calibration is mainly performed for proper working of instrumentation. Technology has evolved in such a way that the medical facilities are brought to doorstep. Nowadays, it has been embedded in the form of application in smart phones. Signal quality must be improved for better results. Specialized equipment are inexpensive for biosensors. These areas are specific areas where advancements are used in the form of gesture, voice recognition.

3. Automation in the field of biomedical instrumentation Automation plays a significant role in every industry in the world and also in our daily experience. Automation is a transformation of work process or a procedure or equipment to automatic rather than much human control or operation. Ford was the first company to establish automation department in 1947, for designing electronic, chemical, hydraulic parts etc. For years, the pharmaceuticals, automotive, chemicals/plastics, aerospace, consumer, and electronics industries have employed automation technology to increase productivity and reduce manufacturing costs. Later in 1970s automation entered the health care industry right from the automatic execution/detection of any parameter in medical image to the automatic production of medical equipment [18]. Automation has taken several advances in every other industry in which the health care industry has never been an exception. With human lives sometimes at stake, quality, reliability, and repeatability are critical factors to be defined for the production of medical products. In the medical field, for the past few decades automation has taken a very huge leap in the development

Biomedical instrument and automation 79 of technologies that aid in advancing the diagnostic and therapeutic procedures i.e., right from automatic interpretation in ECG machine to Robotic surgical procedure and from recognizing a pattern to Artificial neural networks. As time is a critical feature especially in a medical diagnostic field, the concept of automation reduces the time delay for a large extent. With human lives sometimes at stake, quality, reliability and repeatability are critical to the production of medical products. Automation in diagnostic field has largely reduced manual errors and increased the speed and precision of diagnosis. In general, there are three significant components in an automatic process. They are sensor to detect the status of the system, actuator to perform the control commands and the controllers for the program flow and for decision making [19]. Some automation tools are Programmed Logic Controller (PLC), Programmed Automation Controller (PAC), Distributed Control System (DCS), Artificial Neural Network (ANN), Human Machine Interface (HMI), and Motion Control. Automation can either be fixed where the control system is designed to perform one specific task at a time or automation can be flexible where the system is designed or programmed to perform several tasks in a given time duration. Example for automation tools in instrumentation used as an assistive tool. Use of programmed logic controllers in implementation of brainemachine interfaces (BMI) mainly used for the aid of disabled people. This system design is for real-time communication between the BMI and PLCs to control an electrical actuator that could be used in devices to help the disabled. Motor imaginary signals extracted from the brain’s motor cortex using an electroencephalogram (EEG) were used as a control signal. The EEG signals were preprocessed and classified using simplified fuzzy adaptive resonance theory mapping (ARTMAP) in which the classified signals are then translated into control signals used for machine control via the PLC [20]. Use of automation in medical instruments can be described with another example, electronic activity monitoring devices attached to the patients to analyze the physical activities of knee and hip joints. In this case, the motor activities of the individual is analyzed by monitoring the factors like speed, rotary angle, angular position of the knee and hip joints and along with that all fundamental parameters are measured. Signals from the joints are derived through sensors and monitored using the activity monitoring devices. These devices function automatically to record and store the data related to the physical activity of the patient. Thus, automation is involved in governing the activities of the deformed anatomical structures in the human body. Automation used in different medical sectors specifically in diagnosis and treatment processes.

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3.1 Automation in medical instruments Faster diagnosis of any disease or disorder can lower the risk of complications to the patients as treatment can be given at right time. Especially in the field of diagnostics, the automation of instruments is evolving fast with high precision and accuracy. This can allow more tests or diagnosis in a very short span of time and treatment at the earliest. Laboratory equipment used nowadays are mostly semiautomated or fully automated. Equipment such as biochemistry analyzers, cell counters, immunoassay analyzers used today are fully automated where the operator just need to feed the urine or blood sample at one end and the test results are obtained on the other end [21]. Laboratory automation is a latest boon where the handling of chemicals or samples between the analyzing equipment has been automated either through conveyers or through robotic arm depending on the complexity of the laboratory. In medical imaging, automation has taken greater steps in MR imaging, CT scanning and emission tomography imaging. Any pathology in the image found can automatically detect and the severity shall be automatically deduced. For example, in MR imagining that is Magnetic Resonance imaging the tumor can be automatically detected, 3D structure can be visualized and entire dimensionality can be obtained. Nowadays, entire skull can be reconstructed in MR scanning to understand the skull anatomy. This further helps in brain stimulation and research purposes. 3D reconstruction of CT images is one of the latest techniques to exactly locate the deformity and complexity of it [22]. In biosignals, for example in ECG signal processing, automation plays a role in pattern detection and alarming on abnormal patterns of ECG and sudden changes in heart rate. This is more beneficial in neonatal ICU. The software is trained with signal feedback and ANN to improve the accuracy of pattern detection and classification. Automation is used in different diagnostic instruments.

4. Automation in telerobotic surgeries Robotics have paved path for many serious innovations and advancements in different fields. In medical field, use of automatic robotic techniques was introduced to assist the physicians in critical situations of surgeries. Surgical robots are designed with fully automatic structures. A surgical robot is a device which can be controlled using its own power and it can be programmed to aid the surgeons to carry out complex tasks in an easy manner. Hence they can be best described as “extending human capabilities” rather than “replacing human surgeons.” The arrival of surgical robotics in this era has guided a new pathway to “[m]inimally invasive surgery,” where minimized surgical instruments were used to make small incisions during surgeries. As a further advancement of this process, telerobotic surgical systems were developed which led to many novel applications beyond

Biomedical instrument and automation 81 the nuclear and industrial domains [23].The applications of tele robotic techniques in the fields of medicine and health care have reduced the difficulties during diagnosis and treatment of critical traumatic conditions. Origin and structural design of surgical robots are explained in detail.

4.1 Origin of surgical robots The word “Robot” was originated from the Czech word “Robota” meaning compulsory labor. It was defined by the Robotic Institute of America as “It is a machine that performs the mechanical functions of human being but it lacks sensitivity.” The surgical robots came into existence after 1980s and their history is listed below in Table 3.1 as follows. The robots designed for the health care sector is used for either diagnosis, treatment or analytic study showed abrupt variations in the design and automatic mechanisms defined for the functioning of the robots. These surgical robots decreased the mortality rate by effectively increasing the efficiency of the physicians for diagnosis and treatment [24]. There are many different types of robotic surgeries defined for various functions are described.

5. Types of robotic surgeries 5.1 Type-1 supervisorydcontrolled surgery systems In this type of surgical systems, a human surgeon programs the robot. The surgeon performs the necessary actions before the commencement of the surgery and the robots Table 3.1: History of surgical robots. Year

Name of the surgical robots

1985 1988 1992 1994 1997 2001 2006 2009 2009

Programmable Universal Machine for Assembly (PUMA 560) Programmable floor robot (PROBOT) ROBODOC ZEUS Robotic Surgical System (ZRSS) Da Vinci Socrates Robotic Telecollaboration System Da Vinci S Da Vinci Si, iDrive Intelligent Power Unit Micro-robots and emerging field of nano-robots involving biology and engineering SOFIE “Surgeon’s Operating Force-feedback Eindhoven” Surgical Robot The Raven II Amadeus Robotic Surgical System Da Vinci Xi

2010 2012 2012 2014

82 Chapter 3 repeats the same. The surgeon should cautiously watch the robots activities during the entire surgery so that he can intervene during the procedure if anything goes wrong. The main drawback of this type is that, no adjustments can be made once if the input is programmed. These are commonly used during hip and knee replacement surgeries.

5.2 Type-2 shared-control robotic surgery systems This is a unique type of surgical system where the surgeon and robot share the surgery. Here the surgeon does most of the work. The surgeon’s performance is monitored by the robotic system which provides stability throughout the procedure. They are often used in neurosurgery and orthopedic surgery.

5.3 Type-3 tele surgical robotic surgery system Tele surgery is otherwise called as the remote surgery, in which a surgeon performs the tasks at a distance from the patient. He controls the robot using a haptic interface. The three types of Tele surgical Robots are namely (1) da Vinci surgical system; (2) ZRSS; and (3) AESOP robotic surgical system. They are explained below as follows: 5.3.1 da Vinci Surgical System The da Vinci Surgical System was manufactured by the American company “Intuitive Surgical.” It was approved by the Food and Drug Administration in 2000. It makes use of a minimally invasive approach to facilitate complex surgeries. This system uses a 3D high-definition vision system and a tiny wristed instrument that bends and rotates far greater than the human hand which translates the surgeon’s hand movements into precise movements of tiny instruments inside our body thereby enabling the surgeon to operate with an enhanced vision and control 5.3.2 ZEUS robotic surgical system (ZRSS) ZEUS robotic surgical system (ZRSS) consists of an ergonomic surgeon console and three table mounted robotic arms. It makes use of an endoscope through which the surgical task can be performed. Out of these three robotic arms, the right and left arms were controlled by the surgeon and he makes use of the third arm for magnified visualization of the operative field [25]. 5.3.3 Automated endoscopic system for optimal positioning (AESOP) robotic surgical system Automated Endoscopic System for Optimal Positioning (AESOP) robotic surgical system is one of the world’s first surgical robots certified by the US Food and Drug Administration (FDA). This robotic system can be used for positioning and

Biomedical instrument and automation 83 holding the endoscope in a minimally invasive surgery. It can be operated by foot pedals [26].

6. Applications The applications of surgical robots define the different sectors or departments of medicine in which these surgical robots are used. Surgical robots describe the position, location and angle of the abnormality to be removed from the body. They are also helpful for performing minimally invasive techniques like endoscopy and laparoscopy [27]. Open source robots which are designed with a reference database for analysis before indulging in the surgical activities. Surgical robots are not only used as a surgery assistive tool but also used in training the physicians during their course period. Virtual simulation of surgeries for the students of medicine makes them more efficient. Automatic design of analysis in the surgical robots helps them to perform surgeries virtually. Performance of each physician during learning phase can be analyzed [28,29]. Further applications of surgical robots of specified type in which field of medicine are tabulated below given in Table 3.2 as follows:

6.1 PROS and CONS of surgical robots 6.1.1 PROS • • • • • •

Faster recovery time Less pain Tiny Incision The subject can return to their normal activities after surgery within 2 weeks Reduced blood loss Reduces need for surgeon Table 3.2: Applications of surgical robots. Surgical robots

Applications

DaVinci ZEUS or Da Vinci system

Cardiac Surgeries Gastrointestinal surgery, thoracic and laparoscopic surgeries Vascular identification in robotic microsurgical procedure Neurosurgery for both biopsy and microsurgery Transrectal Ultrasound guided biopsy of prostate

Robotic Doppler Micro Probe NeuroArm Transrectal Ultrasound (TRUS) Prostate Robotic System The Raven II Acrobot Sculptor Cyber Knife Robotic System

Open source surgery robot Orthopedics Radio surgery

84 Chapter 3 • Less operative time 6.1.2 CONS • • •

Highly Expensive Needs Regular maintenance Safety is not assured

6.2 The future of surgical robots In the mere future, the surgical robots could be designed in such a way that they can provide a tactile sensation, so that the surgeon can experience a feeling as that of traditional procedures. Moreover, by the recent advancements, it would be possible to maintain a greater distance between the surgeon and patient during the surgery. Telerobotic system range can be increased in future for the benefit of all people in various remote locations [30]. Physical performance of the physician at the locality may not be necessary based on the automatic design of these robots in future. Advancements in surgical robots should mainly focus on the design with fully automatic structures that performs surgeries. Artificial intelligence to be interfaced with the robotic systems is mainly to train and pattern these robots to perform a particular type of surgery. Surgical procedures with very less invasion can be obtained with the advancements in surgical robots. Further, new surgical procedures can also be developed in the upcoming years. Surgical robots also a type of automatic instrumentation designed for welfare of the health sector.

7. Automatic wireless sensor networking in biomedical instrumentation Wireless sensor networking from the biomedical application perspective is defined in detail. A wireless sensor network consists of large number of wireless devices connected together to monitor the various parameters of the human body such as temperature, blood pressure, heart rate, respiration rate etc. The wireless sensors sense the various parameters and through any wireless transmission protocol the data is transmitted to the microcontroller (computer/decision making device) which reads the data and displays the data [31]. The most challenging aspects of designing the wireless sensor networking is transmission range of wireless network, storage of data, signal processing (messaging, routing, topology management), processing of data, signal security, and connectivity. This chapter gives vivid description about the framework of wireless sensor networking for wide range of biomedical applications to the readers and enables the readers to understand easily about the various wireless sensors and its standards of use in health care monitoring.

Biomedical instrument and automation 85 The various physiological parameters of the human body that are measured using wireless sensors are body temperature, ECG (heart rate), pulse rate, blood pressure. The need of wireless sensors in biomedical applications becomes more adequate in recent times. The biomedical wireless sensors acquire the sensor data from the subject, sends the data to the controller. The controller transmits the data to the base station. The base station is wirelessly connected to the receiver. The receiver receives data from the base station and displays the data to the user [32]. Sensor System: The sensor node plays important role in WSN. The sensor system consists of sensor nodes where the data from various subjects are sensed and collected in the sensor node. The sensor system consist of three subelements: subsensor system, controller system, communication system. Subsensor System: The subsensor system converts the analog signal or analog sensed data from sensor into digitized format. The digital conversion of data is more important for transmission signal over WSN. The converted digital data are sent to controller system. The subsensor system is connected to various other sensor nodes. The various sensors nodes receive data from different locations, from different subjects. Controller System: The controller system is also called as processing system. The data collected from various sensor nodes and sensor subsystems are processed in controller system. The controller system controls the sensor nodes and sensor subsystems. The controller system consists of processors of high-speed transmission/reception of data with huge memory capacity of storing the data. The controller system used in WSN should be internet enabled system for transmission of data. Communication System: The communication system is used as decision-making system, that selects the protocol for communication with the receiver side and it selects the quick, reliable, and easily accessible topology for communicating with the receiver. It acts as the node for exchange of messages with the receiver side. Base Station: The base station is very important element in WSN. The Base station is acts as the gateway for host computers. Only through the gateway the sensor node data are transmitted to the user side. All the Base station in the WSN will be connected to any remote server for receiving the data toward the host computer [33]. The sensor system of the wireless network in general is defined in the block diagram shown in Fig. 3.3.

8. Biomedical applications of wireless sensor networking The most common wireless communication used for biomedical applications is Bluetooth and Zigbee, because of its short range of communication (10 m) and efficient power

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Sensor Nodes

Base Station

Host Computer

Figure 3.3 Block diagram of wireless networking system.

management. Moving range of wireless signal/sensor acquisition to Body Area Network (BAN) and Personal Area Network (PAN) for carrying important health care monitoring information are next level of health care monitoring through WSN. Common Requirements for WSN: ➢ ➢ ➢ ➢ ➢ ➢ ➢ ➢ ➢ ➢

Global Synchronization Two-way data communication Data retrieval Encryption Identification Authentication Power Consumption Error Detection/Correction On board or on Chip A/D conversion On board or on chip Microcontroller

8.1 IEEE 802.15.4 The IEEE 802.15.4 is the standard for physical layer and medium of access control for Low Rate PAN. This is the basic standard for Zigbee communication. The various Zigbee applications are House Automation/Monitoring, Industrial WSN, Remote sensing, health care monitoring [34].

8.2 Open system interconnect layered architecture The ISO standard for networking is OSI architecture is shown in Fig. 3.4. The main concept of OSI is that the process of communication between two endpoints in a network can be divided into seven distinct groups of related functions, or layers. Each communicating user or program is on a device that can provide those seven layers of function. In this architecture, each layer serves the layer above it and, in turn, is served by the layer below it. So, in a given message between users, there will be a flow of data down through the layers in the source computer, across the network, and then up through the layers in the receiving computer.

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Figure 3.4 Architecture of open system interconnect layered. Courtesy: https://store.chipkin.com/articles/bacnethow-is-the-bacnet-architecture-designed/.

9. Network topology The Zigbee network layer (NWK) supports different topologies like star, mesh and tree. In a star topology, a single device network is defined as Zigbee coordinator. Zigbee coordinator is necessary tool for start and maintenance of the devices in the network. Other devices termed as end devices communicate with the Zigbee coordinator directly. In mesh and tree topologies, the Zigbee coordinator is mainly used for starting the network and also used to select particular key network parameters. But using Zigbee routers, the network can be extended. In tree networks, routers move information and control messages are sent through the network with the help of hierarchical routing strategy. Tree networks make use of beacon-oriented communication as described in the IEEE 802.15.42003 specification. Peer-to-peer communication is followed in mesh networks. A Zigbee router designed in mesh networks does not emit regular IEEE 802.15.4-2003 beacons. Architecture of Zigbee is shown in Fig. 3.5.

10. Bluetooth communication Bluetooth is the simple wireless radio communication device used in recent trends to avoid the cable and wired communication prototypes. Bluetooth Communication occurs between

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Figure 3.5 Architecture of Zigbee. Courtesy: https://www.researchgate.net/figure/IEEE820154-ZigBee-protocol-stackarchitecture_fig2_265150617.

master and slave radio communication terminology. Each radio has 48-bit address fixed to communicate with master and slave radio communication [35]. Two or more devices connected together form an ad-hoc network called piconets. All units within a piconet have their own frequency and hopping sequence pattern. Only a master can establish the communication link first. The slaves connected to the piconet cannot communicate with other slaves, communication established only with slave and master. A master in a piconet transmits the even number slots and slaves take odd number slots. There will be only one master for a piconet, but different slaves can participate on different piconet to single master at Time Division Multiplexing Bias.

10.1 Bluetooth modules used for biomedical applications The Various Bluetooth modules used in the biomedical applications are HC-05, HC-06, RS 232: TTL, BLE link Bee, BLE mini, Blue SMiRF, Bluetooth mate, JY-MCU, ITEAD BT, etc. Among all the Bluetooth modules the HC-05 is commonly used modules. It is used as Bluetooth Serial port prototype module. It can be easily programmed as master and slave. It is fully enhanced with data rate 3 Mbps modulation with complete 2.4 Ghz radio transceiver and baseband. It used adaptive frequency hopping technique and CMOS technology.

11. Sensing technologies There are two types of sensing technologies available for WSN for patient monitoring and health care purpose. Type 1: Invasive Biosensor and Type 2: Noninvasive Biosensors. For WSN the Noninvasive biosensors are preferred because of its power durability and long connectivity.

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11.1 Invasive biosensors for WSN The invasive biosensors are used in sensing the bio signals and the biological activity of the humans. It can also able to monitor and diagnose the vital parameters of the body. The most important case in designing the invasive biosensors are it should be bio compatible and it has long life time duration. The power source for the sensors should be compatible and should produce less harm to the body. Mostly intercapacitive coupled or piezo electric powered batteries are preferred in implantable biosensors. The size of the sensors should be miniature in size. The implantable biosensors are used as biomarkers for sensing the activity of the human body [36,37]. The various applications of implantable biosensors in recent trends are saliva consumption estimation, glucose monitoring, nerve conduction velocity monitoring, etc.

11.2 Noninvasive bio sensors for WSN The Noninvasive Biosensors are mostly preferred in clinical applications for its low cost, long durability, patient safety, power issues and connectivity toward WSN. The ECG is monitored in the patient monitoring system. The available leads are 3e12 leads. The heart rate is calculated using the ECG. In some patient monitors the respiration rate is calculated using the ECG waveform. It is called as the ECG derived respiration rate. The pulse rate is calculated using the pulse rate sensor. The pulse rate sensor consists of IR Led transmitter and receiver which are placed on the index.

11.3 Respiration rate sensor The respiration sensor is attached to a loop strap and long hook which is wrapped around the chest or abdomen. In various applications, one sensor is always necessary to be placed around the abdomen. Second respiration sensor is optionally placed around the chest. Two sensors are used to analyze abdominal breathing exercises. Unravel the strap and attach it to the abdomen (or torso) in such a way that the sensor is placed in the front. The subject is allowed to breathe out to the extreme and later the sensor is attached to reduce tension. The fit should be tight enough that the strap is fixed even when the subject is in relaxed state. Over extension of the rubber strap around the abdomen is avoided by promoting slack in the strap. The temperature of the body is measured using temperature sensor that consists of thermistor that is used to measure the body temperature.

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11.4 RF and antenna communication RF transceivers are those which receive and demodulate the radio signals. It then modulated to receive new signals. RF transceivers made up of an antenna which receives transmitted signals and a tuner is used for separation of specific signal from all of the other signals that is received by the antenna. Detectors or demodulators derive data or information that was encoded in the system before transmitting the signals. Localized interference and noise are limited using radio techniques. Transmission of new signal is made possible by creating sine waves using oscillators which are encoded and broadcasted as radio signals [38].

12. Selecting RF transceivers Selecting RF transceivers requires a clear understanding about modulation methods and radio techniques. • • • • • •

Amplitude modulation (AM) Frequency modulation (FM) On-off key (OOK) Amplitude shift key (ASK) Frequency shift key (FSK) Phase shift key (PSK)

12.1 Specifications •

• • • • • • • •

Important specifications for RF transceivers include sensitivity, output power, interfacing through communication, range of operating frequency, data rate, measurement of the resolution and maximum transmission distance. Data rate is the number of bits per second that is to be transmitted. Sensitivity is the minimum input signal required. Communication interface is the method used for interfacing the output data or signal to computers. General-purpose interface bus (GPIB) is the commonly used parallel interface. Universal serial bus (USB), RS232 and RS485 are common serial interfaces. Operating frequency is the range of signals that can be received and broadcasted. Measurement resolution is the minimum digital resolution of the signal provided. Maximum transmission distance is the largest distance by which the transmitter and receiver are separated [39].

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12.2 Safety issues The safety issues to be concentrated more on protecting the patient data on base station on transmitter side and also from host side. The connectivity requires end to end encryption of data for safety handling of patient data [40]. The wearable sensors must be carefully designed so that to overcome power and heating issues. Due to continuous monitoring of data the sensor might get heated. The heating issues of sensors should be controlled by maintaining the environment temperature balanced.Among the topics that need detailed tradeoff studies are: (a) (b) (c) (d)

Miniaturization Antenna designs that effectively operate in the surrounding body environment Safety issues based on SAR distributions Communication link characteristics

This study is mainly related to biomedical applications. For example, implanted antennas operate using a biomedical frequency band 402e405 MHz. Attention is given in designing the antennas with miniaturized characteristics and proper functional ability in the tissue environment [41]. The wireless sensor networking in biomedical application plays important role in health care monitoring. The patient monitoring using wireless sensors and wearable noninvasive sensors are more reliable and low cost for easy and long-term monitoring. The primary goal for a best wireless sensor network is secured connectivity, long term connectivity, low power consumption, more storage. One of the recent trend in wireless sensor connectivity is IoT, which is connects the sensors to internet twenty-four-seven using applications and data upload and download done via cloud computing etc. The IoT connects the patient and physician real-time environment.

13. Recent advancements and applications in biomedical instrumentation Biomedical Instrumentation is the branch of instrumentation which deals with the instruments used to measure the physiological parameters like pressure, flow, temperature which are used for diagnosis and continuous monitoring of the patients by the physicians. These instruments with automatic mechanism are also used for treatment and various surgical procedures. Nowadays biomedical instruments become more necessary to physician and the health care devices became mobile and portable. Biomedical instruments can be divided as in-vivo (medical instruments within the body) and in-vitro (medical instruments inside the laboratory).There are various application of

92 Chapter 3 medical instruments in different domain like medical devices in laboratories, Bio signal analysis, medical imaging, clinical engineering, biotechnology, and genetic equipment, implants, and bionics. Due to manual error and requirement of continuous training, there is drastic increase and advancement in evolution of automation in biomedical instruments. Manual testing may lead to slow and prone mistakes this can be avoided by automatic operation of medical devices. Instrumentation is used in different sectors of medicine and therefore application of the instruments with automatic design has increased. This is mainly to rectify the manual error generated and to increase the standard of the medical devices.

13.1 Biomedical instrumentation in medical imaging The physicians are in need of looking inside the body directly or indirectly for better diagnosis. The biological imaging technologies like X-ray, Computer Tomography (CT), MRI, Ultrasonography, Fluoroscopy, Thermography, Positron emission tomography (PET), Single Photon Emission Computed Tomography (SPECT) are designed biomedical instruments used to visualize the body parts. These imaging modalities help the physicians analyze and study the in-depth features of the body parts for diagnostic purposes. X-ray and CT are mainly to diagnose the sections of the structural and anatomical regions with abnormalities. Ultrasonography has developed to a greater extent that movement and position of the fetus can be analyzed in seconds. Ultrasound Imaging is used in diagnosis of various different abnormalities or deformities. Functional Magnetic Resonance Imaging (f-MRI) is specially designed to analyze the neurological disorders. In this type of imaging, functional regions of the organ (brain) under study get highlighted for further analysis. Nuclear based imaging is performed for better analysis of the affected regions that is specially used for tumor identification and diagnosis of cancer. 3D visualization of the internal structures of the body enabled the positioning and analysis of the affected region. Motion imaging helps to study and diagnose of numerous motionbased dysfunctions of the organs. The latest biomedical instruments are used in real-time visualization and it can also be digitalized for further reference. Real-time visualization is used in techniques like laparoscopy, endoscopy and laser surgeries to assist the physicians in treating the disorders or abnormalities. Real-time visualization using capsule endoscopy, a best example for automatic imaging techniques defined to small capsule which is shown in Fig. 3.6. Microchips embedded with camera lens, transmitter and receiver sensors are used to capture the images internally [42]. Imaging techniques has advanced in such a way that performs scanning, analysis and defines the state of the individual.

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Figure 3.6 Capsule endoscopy. Courtesy: https://www.dicardiology.com/article/capsule-endoscopy-systems-safetypatients-cardiovascular-implants.

13.2 Biomedical instrumentation in medical devices Medical devices can be classified as diagnostic and therapeutic devices. The diagnostic devices are used to diagnose and monitor the vital parameters derived from the body. The diagnostic devices include thermometer, Bio signal recording instruments (ECG, EEG, EMG, and so on), glucometer, pulse oximeter and patient monitoring system. The latest technology like Energy Loss Optimization, Fuzzy Logic is used to Control Motor Drivers for faster and accurate output. Automatic designing is to control the operations of the instruments. In medical instruments, internal circuits are designed for performing the functions of recording and analysis of data for diagnosis. When these instruments are used for treatment purpose, the circuits are in built fed with all the necessary steps required for the process [43]. Laser surgeries can be defined as the best example in which the instruments define the range, distance, angle, position, type of radiation and amount of exposure for particular type of abnormalities or medical condition described after diagnosis. Latest advancement in automated medical devices is ultrahigh resolution image of tumor can be obtained using MRI and high precision surgery can be achieved to remove the located tumor. But doing this simultaneously is high impossible [44]. This can be achieved by piezo motor powered MRI Robot.

13.3 Biomedical instrumentation in tissue engineering Tissue engineering is a field which deals with combination of cells based on engineering methods or techniques. Instruments used in this field are highly definite and used for many applications. One among them is development of tracheas and solid jawbones from the stem cells. Research is going on to develop the complete bio artificial organs which can alter or replace the damaged natural organ. This is due to the advanced biomedical instrumentation [45]. Genetic manipulation or gene modification for traditional breeding is possible because of medical instruments.

94 Chapter 3 Automatic design of equipment’s helps to make fine modifications in the genetic combinations or amino acid codes. Novel drug manufacturing and delivery is also achieved using medical instruments. Drug designing also requires automatic control mechanism for illustrating the effect of the drug under research [46]. Open surgery procedures for biopsy leads to tissue trauma and high recovery time this can be avoided by using latest automated biopsy system [47]. This automated system consists of disposable probe integrated with cannula and tissue chamber and a driver unit to produce power to rotate the cannula and cutter. Automatic instrumentation is used in tissue engineering helps to easy diagnosis even at cellular levels.

13.4 Biomedical instrumentation in implants and bionics An implant is a replace of damaged or missing biological organ or a medical device that assists a biological organ or used for diagnosis purpose. Mostly implants are in the form of prosthetic devices which is mainly designed to replace the amputated body parts. There are different types of implants that involve in delivering medication or drugs, monitoring of body vital parameters and in different cases acts as an assistive tool to damaged organs and tissues. In general implants are formed from skin, bone or other body tissues. These implants can be produced synthetically using metal, plastic, ceramic or other materials. Implants can be used permanently, or they can be removed if they are not required further. For example, stents or hip implants are defined to be permanent. But supportive ports or screws used to alter or repair the broken bones can be removed when they are not needed. Designing of implants include instruments defined for analyzing the structure of the implant through software and reconstructing it through the required materials. Example for commonly used metallic implants is the knee joints. These are implanted for patients with knee disorders especially in the case of total knee replacements [48]. 3D printers are efficient instruments used for constructing the implants or prosthetic devices. The implants like artificial pacemaker and cochlear implants are electronics embedded implants. These implants possess features which make them biocompatible and described with automatic circuits for rhythmic functioning [49]. Automatic alarm circuits are also defined for these electronic implants to indicate any type of internal faults. External artificial body part that has the same biological and functional structure to replace the missing biological organ is called bionics. Example for this kind of system is bionic hand controlled by myoelectric signal which is shown in Fig. 3.7.

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Figure 3.7 Myoelectric controlled prosthetic arm. Courtesy: https://www.nibib.nih.gov/news-events/newsroom/ implantable-sensors-improve-control-prosthetic-limbs.

13.5 Biomedical instrumentation in clinical engineering Laboratory instruments are used to perform tests or to obtain data from the samples. Instruments like colorimeter, spectrophotometer, centrifuge, auto analyzer, and fluoroscopy are used in laboratory to find the essential parameters and range of chemical compositions to describe and diagnose the medical condition. Computed tomography scanners, rapid HIV tests, real-time ultrasonography laparoscopic surgery, and electronic health records are most common and notable examples for automatic instrumentation in the laboratories [50]. Technology has developed in drastic range that long lines in the pharmacy can be avoided by using fast robust compact and high torque automatic prescription dispensing system. This system distributes tablets and capsules, ointments with high accuracy. Computerized billing and storage of patient details is mainly performed for storage and retrieval of the data for future analysis. The automated histology process provides fast three dimensional views of molecular structure.

13.6 Biomedical instrumentation in neural engineering Neural engineering is the field of engineering interfaced with biological techniques to replace, repair and analyze the neural systems. The aim of this type of biomedical field is to analyze and provide solutions to neuroscience-related problems. This field of engineering also deals with rehabilitative solutions for nervous system conditions. The emphasis on engineering and quantitative methodology applied to the nervous system distinguishes neural engineering from traditional areas in neuroscience such as

96 Chapter 3 neurophysiology. The association of neuroscience and engineering defines neural engineering separately from other engineering disciplines such as artificial neural networks. Instrumentation in neural engineering deals with several fields such as brain computer interface, neuroelectronics, neurotechnology, neuroimaging, neuroinformatics, neurorobotics, neuromechanical systems, neuralcontrol, and so on. Instruments in all these fields are used for interpreting the neural structures. Interpretation is mainly to analyze the anatomical abnormalities or structural deformities in the nervous system [51]. Recent advancements in the neural engineering field illustrate the use of automatic computer interfaced prosthetic limbs for amputated individuals. Computer based controlling system designed for the prediction and diagnosis of diseases. Artificial neural networks replicate the nature of the biological neurons and defined new techniques like classification and object recognition. Artificial intelligence plays a major role in automatic designing of instruments for diagnosis and assisting the physicians during critical surgeries. Neural engineering is full automated and computerized to enhance the state of the health care sectors.

13.7 Biomedical instrumentation in rehabilitation engineering Rehabilitation is a field for the designing, repairing and replacing of amputated limbs or assistive tools for physically challenged individuals. Different prosthetic and orthotic devices are designed as a assistive technology. Automated assistive devices are designed with proper gait and appropriate motor functions for the physically challenged persons [52]. There is more advancement in rehabilitation engineering with production of motorized wheelchairs with automated controlled gears. Control mechanism defines the use of servo motors and actuators in controlling the device structures for proper functioning. Example for these devices can be ankle foot orthotic exoskeleton which acts an external support for the stroke patients with diminished locomotary actions. There are automated robot assisted rehabilitation devices to assist poststroke subject for recovery. Ultrasound or Infrared based walking canes for visually challenged persons to identify or recognize the objects before is designed which fully automatic and works at the press of a switch [53]. Furthermore, innovative advancements are made in the field of rehabilitation. Instrumentation designing with automatic mechanism is also useful in the developing prosthetic and orthotic devices. Different rehabilitation devices are designed and developed for the use of different physically challenged personalities.

13.8 Applications of automation in biomedical instrumentation Best example for advancements in the field of instrumentation is embedding health care modules to a drone and is widely used in delivering medical products or drugs to remote

Biomedical instrument and automation 97 locations. Medical drones are used to carry medical equipment’s to regions prone to disaster and damage. Drones are used in many ways to improve and provide medical facilities to all regions even in remote areas of the world. Automation in biomedical instrumentation is more effective for increasing the standard of the health care systems [54]. Safety also plays a major role in developing the health care sectors. Electrical and radiation safety of the instruments or equipment’s is necessary for the complete protection of the patient. Electrical safety analyzers are designed for determining the leakage current, voltage, and can also be used to define the damage in any internal circuits. Electrical nature of the instrument is described with these safety analyzers. Automation is also adopted by these instruments for accurate measurements of the flow of current or voltage to the patient. Transmission of voltage from patient to system is analyzed using these analyzers. Safety analyzer is specially designed for each instrument based on the equipment’s internal design and features [55]. This safety analyzer for ECG machine defines the leakage current and voltage in the electrodes and equipment system. Radiation safety is also determined from the radiation-based instruments like CT, X-ray, and so on. Radiation emitted by these equipment’s are analyzed and any leakage of radiation will be detected through radiation safety analyzers. Automatic equipment’s for the determination of radiation is developed [56]. Advancements in these detectors are mainly focused in to define a cost effective design of detectors for analysis of radiation. Medical instruments have led to various advancements in medical field. Automation led to the development of different new equipment’s which is used for the diagnosis and treatment. Technology played a vital role in implementing automatic mechanism in the designing of the equipment. Computerized technology sophisticated the physicians and helped the patients with proper health care and medical facilities. Thus, automation in biomedical instrumentation is used in various applications in different fields of medicine.

14. Conclusion Automation can be defined as a technological field in which any process or technique is performed without any or minimal manual assistance. Integrating people and systems to achieve automation is not a simple matter. Cognitive nature of the people is interfaced with the computerized automatic mechanism in the devices for appropriate advancements in the field of automation. Automation is used in instruments of various fields which is developed and designed for functioning in different streams of engineering. Automation deals with a wide range of applications from thermistor controlling the simple digital

98 Chapter 3 thermometer to a large industrial control system with several input measurements and output control signals. In control complexity, automation of a system can range from multi-variable high level systems to simple ONeOFF switch. Automation used in the medical instruments is specially designed for the development of the health care sector [57]. Medicine is a field of branches dealing with biological components functioning and anatomical features. Each branch makes use of numerous automatic systems for various purposes like diagnosis, analysis, research study, treatment and so on. Automation designed for all the medical instruments is to make the health care more effective and efficient. Biomedical instrumentation refers to the equipment’s specially designed with sensors and circuits for diagnosis and treatment of different kinds of abnormalities. Application of automatic mechanism occur in different medical instruments such as recording instruments like ECG, EEG, EMG for diagnosis of abnormality related to the regions producing the electrical activity. Automation is applied to all laboratory equipment to test numerous samples at single cycle, thus leading to time consumption. Rehabilitation another field of devices filled with automatic techniques that act as an assistive or replacement tool [58,59]. Different fields of medicine make use of automation for the various sources of functions even in biological nanotechnology for delivery of drugs and necessary minerals. Internal circuits, time delay, alarming patterns and programmed control systems together define automation in instruments. These setup organized in a medical equipment should be more precise as it deals with the life of an individual. Automation is necessary for easy and early diagnosis and treatment of life-threatening diseases or disorders. Continuous calibration and monitoring of the instruments is required to prevent any disaster. Instruments should be handled with care and proper guidance [60]. Rules and regulations have been formulated for the instruments designed for the specific task of diagnosis, treatment or analysis. Medical instruments with automatic design and mechanism infringed in are useful for the development and betterment of the health care and medical sector. Recent advancements in the field of instrumentation led to new era for innovations in automatic designing. Research and study is going on with all kinds of instruments to convert them fully automatic. Thus, medical instruments developed and designed with automatic control systems are useful to a larger extent for diagnosis and treatment.

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CHAPTER 4

Performance improvement in contemporary health care using IoT allied with big data Mamata Rath1, Vijender Kumar Solanki2 1

Birla School of Management (IT), Birla Global University, Bhubaneswar, India; 2CMR Institute of Technology (Autonomous), Hyderabad, India

Chapter Outline 1. Introduction 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 1.10

103

Outline of IoT and big data 105 Technology modernization and quality as a challenge in health care systems Availability of health care information in social media 108 Smart applications related to health care systems using IoT 108 ICT and big data in health care development 110 Cyber physical cloud computing and health care approaches 111 Big data analytic methods in health care 111 Decision making tools and logic implementation in big data 113 Quality assessment model in big data 114 Health care monitoring frameworks 115

106

2. Conclusion 117 References 117

1. Introduction The needs of health care administrations has been developing significantly speedier than different ventures. An aging populace influences the idea of abilities and administrations, so social insurance experts must be set up to give conveying the correct human services administration to fit the necessities of an individual, anyplace, whenever. Many developed frameworks have been built-up by eminent researchers that depends on the best and automated process with intention to enhance the current patient care methods [1], and to present new capacities in view of the huge information upset that guarantees the framework is savvy has been developed. Along these lines, the underlying procedure of Handbook of Data Science Approaches for Biomedical Engineering. https://doi.org/10.1016/B978-0-12-818318-2.00004-0 Copyright © 2020 Elsevier Inc. All rights reserved.

103

104 Chapter 4 necessity examination for the smart work process of the healing center is a critical procedure and a novel Smart Flow Model for smart healing center in view of a few explanatory methods utilized for social event prerequisites [2]. In addition, this work process gives a superior method to comprehend the care boggling healing center coordination system and streamlines the doctor’s facility work process less demanding. Simulation output show that the proposed Smart Flow Model works faster than the current work processes. During business processing when business logic is incorporated to make decisions, the subjective relationship among various group of applications gave as inclination utility is important to be displayed to give certainty inclination added substance among it diminishing ambiguity and create better utility inclinations estimation for good quality forecasts [2a]. Most models in Decision supportive networks are accepting such criteria as free. Diverse kinds of data (time arrangement, phonetic qualities, interim data, and so forth) forces a few challenges to data examination due to pre-preparing and standardization forms, which are costly and troublesome when data sets are crude and imbalanced. These issues have been featured and anticipated and connected to health watch over elderly by blending heterogeneous measurements for giving health care forecasts to elderly at home [2]. It has used group learning as multi-grouping strategies on multi-data streams that are collected from multi-detecting devices. Subjectivity (i.e., benefit personalization) would be analyzed in view of relationships between various analytical structures that are mirroring the system’s individual settings, for instance in nearest neighbor based configuration. A portion of the properties inadequacy likewise may influence the estimation precision [23]. Qualities additionally talk about these difficulties in granular registering and choice emotionally supportive networks look into spaces. The present condition of workmanship has been discussed and concentrated on health care hazard investigation with cases from our tests. New prospects for healthy mind checking is induced by the expansion of Internet of Things (IoT) and big data, and in addition the universal idea of little wearable bio sensors [24]. Fig. 4.1 shows big data applications in various fields out of which health care sector is very important. Numerous challenge presently can’t seem to be routed to make a true decision and to create adaptable framework for health & mind checking big data investigation on IoT based Health mind framework is proposed [2] and it utilizes IoT based health mind observing framework that contains “Web of health sensor things". These things create gigantic volumes of data that couldn’t be dealt with by the doctor. The physician’s concern is that they have to settle on basic choices about their patient’s health from these gigantic volumes of health data. He/she needs to isolate the data around one specific patient from the surge of health mind data landing from the monstrous number of patients. Intel Galileo Gen 2 is going about as an IoT operator and is utilized to convey the health data of patients into the Cloud. This could deal with the expanding volume of

Performance improvement in contemporary health care using IoT allied with big data 105

Figure 4.1 Big data applications in various fields.

health data, ingeniously share the data crosswise over health care frameworks and give feasibility to big data examinations [25]. Online alerting of patient health data is a critical exercise in Big data which is important in many proposed work. The big health sensor data are investigated utilizing the Hadoop system. Since the reaction time of the proposed framework is less, it is reasonable for ongoing cautioning [3]. The chapter matches with the scope and objective of the book. It performs a survey and highlights the major issues related to health care in current advanced technological environment particularly based on IoT and big data analytic techniques. It focuses on quality of service in health careebased real time applications. Under this subject communication in wireless body sensor network are discussed briefly [4]. Smart applications related to health care are described and challenging issues are highlighted. Next, design of various smart IoT framework in health care are focused as case study. The next section describes health care in IoT network and smart community. SDN Architecture for health care systems in IoT is discussed here. IoT in a smart community is described next with various examples. Ethical issues related to health care are mentioned in a very significant manner.

1.1 Outline of IoT and big data IoT is a transformational technology for electronic security frameworks. In numerous regards, business and private security items were forerunners to IoT and keep on sharing numerous critical qualities of the class. All things considered, the fast drop in cost for IoT gadgets and the billions of new IoT gadgets that are relied upon to be introduced in the following 5e10 years make it a power to be battled with as far as how we consider electronic security frameworks and the market all in all. IoT additionally enhances the industry tasks in light of the fact that the most every now and again referred to shopper

106 Chapter 4 profit of IoT in the house is for security applications. By and large, the ascent of IoT increment value for security purchasers on the grounds that the financial aspects of the IoT industry will apply descending cost weight [5] on security segments, even as they turn out to be more competent and give more highlights. Developing countries where health careerelated information are not completely used to help and enhance health care results is because of alienated Health care Information Systems (HISs). The Namibian health care scene, scratch capacities, data frameworks, and the difficulties they confront has been discussed in Ref. [6]. Namibia has a double health care framework with an open health care part that works in parallel with a secretly subsidized health care arrangement. The data and data being gathered or firmly pertinent to the Namibia HIS are assembled into three principle classifications gathered, stockpiling and oversaw as of now in different HIS that are divided and crumbled. Thus, there is a requirement for a coordinated HIS. We investigate two conceivable methodologies, and related reference engineering with their difficulties. Another way to deal with enhanced Lambda design restrictions has been proposed [7] utilizing abilities and ideas of controlled engineering and virtualization. The intensity of machine learning has been enormously perceived in changing different ventures and health mind applications. The reason that Amazon, Netflix, and Google have changed their enterprises and have inserted learning all through each part of what they do. This chapter describes application of some emerging technology such as IoT and big data analytics [8] in management of smart health care systems. After getting inspiration of the above discussed technologies at the second part of this chapter a smart health care execution system (SHES) has been proposed based on soft computing inspired smart intelligence and it has been simulated using NetSim Simulator. The simulation result shows improved performance in terms of quick message communication between smart health care centers and increasing the throughput of the proposed system.

1.2 Technology modernization and quality as a challenge in health care systems These days, electronic health mind services have been accepting increasingly consideration, because of the generously expanding number of superior processing gadgets interconnected with restorative sensors indicative frameworks through heterogeneous network advances. Portable health mind is a subsection of the eHealth field, expecting to utilize cell phones, wearable or implicit sensors with Body Area Networks and the vastest size of wireless correspondence advances to upgrade conventional restorative services. Propelled portable advancements assume an inexorably vital part in health care frameworks particularly in situations of tele-conference telediagnosis and versatile patient checking. Normally, portable health care services require strict, therapeutic level QoS and QoE arrangement [9]. The constant utilized instances of portable health care services, for

Performance improvement in contemporary health care using IoT allied with big data 107 example, remote versatile patient checking, telecare and remotely guided surgical mediation require significantly higher certifications (e.g., little deferral and jitter, quick reaction time, and low bundle misfortune). Numerous portable health care situations depend on wearable body key (e.g., ECG, heart rate screen, and ultrasound) or inherent sensors of advanced mobile phones (e.g., high-determination camera and whirligig). The very shifting qualities of various versatile health care applications in methods for the required network assets, QoS/QoE prerequisites conjure elaboration of cutting edge network administration structures [10]. These actualities persuaded us to outline and actualize a network-helped wireless access network determination system for mHealth (mobile health care) services, which can choose the fitting accessible network utilizing a multicriteria choice motor. There are some exploration commitment in which the portable health care framework depends on the Distributed Decision Engine (DDE) and the Network Information Service (NIS) giving both static and dynamic parameters of the accessible human services correspondence networks [11]. There are some methodologies in which the arrangement is executed for Wi-Fi networks. E-Health mind frameworks additionally gives a proficient domain to therapeutic quality information transmission in different portable health care situations [12] (Table 4.1). Table 4.1: Description of some important contribution of big data associated with other technology. Sl. No

Literature/author

Year

1

Zuhra et al. [13]

2017

2

Lomotey et al. [14]

2017

3

Farahani et al. [3]

2018

4

Hossain et al. [4]

2017

5

Harbouche et al. [15]

2017

6

Adame et al. [6]

2018

7

Lloret et al. [7]

2017

8

Olayinka et al. [8]

2017

9

Knoppers et al. [16]

2017

10

Pramanik et al. [10]

2017

Details of big data application Use of WBSN (wireless body sensor network and protocols in detection of body anomalies Wearable IoT devices and intelligent detection of health problem in distributed health information system (DHIS) Fog computing approach and IoT based e-health care system Internet of Things (IoT) based health prescription assistance software for health diagnosis Use of WBN (wireless body sensor network) for health care and correct monitoring system A hybrid monitoring system for smart health care using RFID and wireless sensor network Improved protocol for efficient and smart nonstop ehealth controlling & monitoring Big data knowledge implementation in global health education system Rules related to ethics and systems in big data health care analysis Big data enabled health paradigm and issues in smart city health applications

108 Chapter 4

1.3 Availability of health care information in social media Modern tools are available in social media to assess the correct model for postcommitment and expectations on social media like Facebook. Also, it gives knowledge into significant markers that prompt higher commitment with health-mind posts on Facebook. Both directed and unsupervised learning procedures are utilized to accomplish this objective. This exploration expects to add to technique of health-mind associations to connect with consistent clients and assemble preventive systems over the long haul through useful health-mind content posted on Facebook. In the present Indian situation, health care data is freely kept up by doctor’s facilities, foundations and not promptly open in a brought together, educated way. This extraordinarily restrains the health suppliers’ endeavors to enhance quality and proficiency. The issue of data from numerous sources into one place in real time situation has been fathomed [17] which can be genuinely life sparing. Additionally, low proportion of specialist to understanding and the low per capita wage in India climbs the medicinal costs consequently expanding the patient’s detachment to get appropriate health mind in their scope particularly for individuals in the provincial regions. A method by which the extension between the patients and specialists can be gapped and how patients can be dealt with at a lower cost is the prime concern. It likewise centers around the improvement of a versatile/web application, through which patients sends their symptomatic question to the specialists through a server [17a]. The versatile application will be furnished with medical aid guidelines, as per the nature and seriousness of the side effects, either the patients are coordinated to separate divisions or given crisis help for promote treatment. Inside the time gigantic measure of data is gathered from clients and specialists, this big data will be utilized to prepare machines to computerize the assignments to some degree. The data picked up from breaking down enormous measures of accumulated health data can give helpful knowledge to enhance quality and proficiency for suppliers and back up plans alike [17b]. This influences the patients to connect for health care arrangements effectively and economically and makes health care a simple reach for the unprivileged too. In this manner, this brought together model can fill in as a data accumulation, conveyance and in addition a scientific device in the health care space.

1.4 Smart applications related to health care systems using IoT With the taking off enthusiasm for the IoT, some human services suppliers are encouraging remote care conveyance using wearable gadgets. These gadgets are utilized for constant spilling of individual therapeutic information (e.g., vitals, solutions, sensitivities, and so on) into social insurance information frameworks for the reasons for health care checking and proficient conclusion [4]. In any case, a test from the viewpoint of the doctors is the failure to dependably figure out which information has a place with

Performance improvement in contemporary health care using IoT allied with big data 109 who progressively. This test exudes from the way that medicinal services offices have various clients who claim different gadgets, and consequently making an information source heterogeneity and complexities for the gushing procedure. As a feature of this research, this issue has been streamlined by proposing some wearable IoT information gushing design that offers traceability of information courses from the beginning source to the health care information framework [20]. While examining data where the connections between factors are not completely comprehended, it is commonplace to participate in visual investigation [26]. In any case, this is moderate and physically concentrated, and fascinating patterns could conceivably be missed. Editorial in centers around examples and areas inside the data that are fascinating, for example, noteworthy bunches and exceptions. It introduces a novel iterative k-implies grouping calculation to proficiently distinguish bunches in substantial datasets. This encourages quick visual investigation of new datasets. The system has been shown by playing out a nitty gritty examination of open health mind data discharged by the US Government and New York State. Iterative k-implies calculation has been utilized to recognize groups of patterns in labor compel cooperation to introduce a remarkable point of view by zone of restorative claim to fame over a 50-year time frame. Claims to fame, for example, nurture experts have seen a huge increment in the quantity of specialists with respect to inner pharmaceutical. These days, there is a consistently relocation of people to urban zones. Health-mind benefit is a standout amongst the most difficult perspectives that is incredibly influenced by the huge flood of individuals to downtown areas. Thus, urban areas around the globe are putting vigorously in advanced change with an end goal to give healthier bioanalytical systems to individuals. In such a change, a huge number of homes are being furnished with keen gadgets (e.g., brilliant meters, sensors, etc.), which create enormous volumes of fine-grained and indexical data that can be investigated to help shrewd city administrations. A proposition of a model that uses shrewd home big data as a methods for learning and finding human movement designs for health mind applications has been shown [18]. It features the utilization of regular example mining, group examination, and expectation to gauge and dissect vitality use changes started by inhabitants’ conduct. Since individuals’ propensities are for the most part distinguished by ordinary schedules, finding these schedules enables us to perceive strange exercises that may demonstrate individuals’ troubles in taking tend to themselves, for example, not planning nourishment or not utilizing a shower/shower. The need to break down transient vitality utilization has projected designs at the apparatus level, which is specifically identified with human exercises. For the assessment of the proposed tool, During assessment, the data from keen meters are recursively mined in the quantum/data cut of 24 h, and the outcomes are kept up crosswise over progressive mining works out. The consequences of distinguishing

110 Chapter 4 human movement designs from apparatus utilization are displayed in detail [19] alongside the precision of short and long-haul forecasts.

1.5 ICT and big data in health care development In this quickly changing world and with the progress of science and technology, the health and patient care part get more extensive consideration from building and health mind experts. Telemedicine and e-health are the most centered territories and increased greater improvement with the mix of ICT and Big data [20]. The productivity of e-health and telemedicine enormously depend on the kind of correspondence fused. The determination of less expensive and more secure correspondence with great administration quality is a testing errand in framework outlining. The blend of wired and remote correspondence medium is ended up being more proficient and more secure for data transmission. The idea of e-health and telemedicine makes down the health business with the accessibility of remote patient observing, catastrophe administration, and control and secure data transmission from a remote area to health proficiency. In this manner, the idea of e-health and telemedicine alongside the usage of focal points of ICT and big data has been investigated [19]. It likewise addresses the deterrents identified with the correspondence framework, for example, framework cost and its standard. Data technology has progressed amid the most recent five decades to the phase where its effect is being felt by the general public in each administration that it gets from media, business, health mind, shopper gadgets, vitality and power, and transportation domains [19a]. Amid this course of human-technology collaboration tremendous measure of data and learning exchange happens straightforwardly between specialist co-ops and their customers, and in addition by implication between customers. Since human propensity is to “investigate” its past with a specific end goal to foresee the “future,” monitoring this powerfully gushing voluminous heterogeneous data, called big data (BD), and examining it for significant revelation of learning that prompts esteem included business turns into a critical research action [21]. It is in this setting research in BD figuring has developed. Important choices can be constructed just with respect to a large information disclosure, which thus requires a decent comprehension of the attributes of the aggregated data, a fitting grouping of this enormous gathering, and a proficient investigation of it. Healthmind part is a basic framework since its administrations influence the lives of humans and the absence of administration coherence might be unfortunate to the economy and human lives. The huge measure of data gathered by this part from its customers is organized into Electronic Health Records (EHR), which is BD and is utilized alongside pharmaceutical and administrative data in giving health administrations. More BD is produced while controlling administrations and estimating their effects on customers subsequent to

Performance improvement in contemporary health care using IoT allied with big data 111 regulating the administrations. In this huge setting explore the sorts and wellsprings of Health Care BD (HBD), its attributes, and give a characterization of it.

1.6 Cyber physical cloud computing and health care approaches The Cyber-Physical System (CPS) is an emerging computing technology that involves in sensing, computing, controlling, and communication between physical components (e.g., smart sensors, devices, systems, and human beings) and cyber components [21a] (e.g., cloud and big data centers). The sensing, controlling, and interaction have significant promises toward the realization of the current and future therapy system, where cloud resources and data centers are expected to process complex therapeutic heterogeneous big data. Although, the CPS has a great potential for such a sensing and controlling of therapy, however, energy efficiency is crucial for such a therapy system for its sustainability, especially for elderly people who cannot physically optimize energy consumptions. To this end, this article proposes an energy-aware cyber-physical therapy system (T-CPS), which incorporates smart things and devices in both the physical and cyber world for therapy sensing [21b]. To provide energy-efficient affordable therapeutic services, the T-CPS framework uses multimodal sensing for the provision of therapy sensing, therapy playback, annotation, visualization, and energy efficiency. The framework was evaluated by real subjects along with several therapists. Test results show the usefulness of the proposed T-CPS framework. Individual or personal health record (PHR) has been created as a promising arrangement that permits patientespecialists associations in an extremely successful manner. Cloud innovation has been viewed as the unmistakable possibility to store the delicate medicinal record in PHR, however to date, the security insurance gave is yet insufficient without affecting the reasonableness of the framework. In this paper, we give an agreed response to this issue by proposing a general system for secure sharing of PHRs. Our framework [21c] empowers patients to safely store and offer their PHR in the cloud server (for instance, to their carers), and moreover the treating specialists can allude the patients’ restorative record to masters for inquire about purposes, at whatever point they are required, while guaranteeing that the patients’ data stay private.

1.7 Big data analytic methods in health care The term biosensors includes gadgets that can possibly evaluate physiological, immunological and behavioral reactions of domesticated animals and various creature species. Novel biosensing approaches offer exceedingly particular monitoring gadgets for the particular estimation of individual and numerous parameters covering a creature’s physiology and additionally monitoring of a creature’s situation [22]. These gadgets are not just very particular and sensitive for the parameters being dissected, yet they are

112 Chapter 4 additionally solid and simple to utilize, and can quicken the monitoring procedure. Novel biosensors in domesticated animals administration give noteworthy benefits and applications in ailment location and confinement, health care monitoring and recognition of conceptive cycles, and in addition monitoring physiological prosperity of the creature by means of examination of the creature’s condition. With the improvement of incorporated frameworks and IoT, the consistently monitoring gadgets are required to end up noticeably reasonable [22a]. The data created from coordinated domesticated animals monitoring is foreseen to help ranchers and the farming business to enhance creature productivity later on. The data is required to lessen the effect of the domesticated animals’ industry on the earth, while in the meantime driving the new wave toward the changes of suitable cultivating procedures. This audit focuses on the developing mechanical headways in monitoring of animal health care for definite, exact data on productivity, and in addition physiology and prosperity. Biosensors will add to the fourth transformation in horticulture by fusing creative innovations into savvy analytic techniques that can mitigate the possibly disastrous impacts of irresistible flare-ups in cultivated creatures. There is a comprehension of how we can correctly implement the government projects and health plans in smart cities. Far reaching measure of heterogeneous information is made by these workplaces. Nevertheless, without validating the correct information and simulation the recovery system, it is not wise to implement them. Huge Data Analytics using Hadoop accept a convincing part in performing critical ceaseless examination on the tremendous volume of information and prepared to foresee the emergency circumstances before it happens. It portrays about the enormous information use cases in human administrations and government. Among the most widely recognized and ceaseless issues in the social insurance framework worldwide is the swarming of crisis rooms (ER); prompting numerous genuine inconveniences. Ruler Faisal Specialist Hospital and Research Center used health care analytics techniques to recognize regions of inadequacy and propose potential upgrades to ER execution. More arrangements ought to be inspected, for example, group triaging, patients palmar filtering, and setting a doctor in triage. Additionally, more pointers ought to be monitored, for example, the adequacy of ER treatment including the rates of revisits. In the course of recent years, data has been developing inestimably in all business areas. While numerous enterprises are effective in performing big data examination to profit by data sets, health-mind division has begun to find a way to push ahead. Health-mind suppliers and speculators are currently putting resources into data diagnostic abilities to effectively profit by these data sets. This change over to big data practice causes them to have a superior comprehension different aspects of this versatile technology. To oversee and coordinate different unstructured health-mind big data sets in a safe situation and to create valuable information from these unstructured data sets and to make an interpretation

Performance improvement in contemporary health care using IoT allied with big data 113 of the learning into a working culture will solve the problem of many challenges. The fundamental focal point of research venture work is to assemble an application framework for early recognizable proof of ailments. This application framework can be an exceptionally supportive device for the health mind specialist organizations to enhance both general quality and effectiveness in the health mind zone. The application framework is constructed, utilizing Naı¨ve Bayes (NB) order calculation running over Apache Mahout, to suggest the health states of clients, readmission rates, treatment streamlining, and unfriendly occasions. The current health mind approaches are generally in view of standard relapse techniques, which have confinements. The focused research work manages breaking down and utilizing new big data systems. NB grouping calculation has been utilized for diagnosing the ailments and giving fundamental medications recommendations. Once the malady is recognized, conveying the right care to the patients upgrades the treatment cost. Likewise normal future of individuals is fundamentally expanded by treating individuals with the suitable care from beginning times. The health care industry has given careful consideration to the potential advantages to be picked up from big data. There many views about the applications of big data in medical fields, but the real challenge remains in secured and safe implementation of it with fast and reliable diagnosis. To address analytical engineering approaches and functionalities of big data, and its prescient ability to help health care administrators will create many successful big-data-based strategies. Health care associations ought to react deliberately to the difficulties they look in the present health care systems by utilizing analytical tools.

1.8 Decision making tools and logic implementation in big data Finding data patterns from big data brings attention to most of the applications because of its significance in finding exact patterns and highlights that are utilized as a part of opportunity of decision making. The difficulties in big data investigation are the high dimensionality and multifaceted nature in data portrayal. Granular processing and highlight determination are among the test to manage big data examination that is utilized for decision making. These difficulties has been talked about and dissected and an introduction on gathering learning for health mind hazard forecast has been done. During business processing when business logic is incorporated to take decision, the subjective relationship among various group of applications gave as inclination utility is important to be displayed to provide better understanding. Most models in decision emotionally supportive networks are accepting criteria as free. Diverse kind of data (time arrangement, phonetic qualities, interim data, and so forth) forces a few challenges to data examination due to prepreparing and standardization forms which are costly and

114 Chapter 4 troublesome when data sets are crude and imbalanced. These issues have been featured and anticipated and connected to health-watch over elderly, by blending heterogeneous measurements for giving health mind forecasts to elderly at home. It has used gathering learning as multi-grouping strategies on multidata streams that gathered from multidetecting gadgets. Subjectivity (i.e., benefit personalization) would be analyzed in view of relationships between various analytical structures that are mirroring the system of individual setting, for instance in closest neighbor-based connection examination fashion. A portion of the properties inadequacy likewise may prompt influence the estimation precision. Qualities with inclination requested area relations properties end up one angle in requesting properties in harsh approximations. Research diagrams issues on Virtual Doctor Systems, and features its development in collaborations with elderly patients, additionally talk about these difficulties in granular registering and choice emotionally supportive networks look into spaces. In this discussion I will introduce the present condition of workmanship and concentrate it on health mind hazard investigation with cases from our tests.

1.9 Quality assessment model in big data Technology progressions in health mind informatics, digitalizing health records, and telemedicine has brought about fast development of health mind data. One test is the way to adequately find valuable and vital data out of such enormous measure of data through methods, for example, data mining. Anomaly location is an ordinary strategy utilized as a part of numerous fields to break down big data. Be that as it may, for the huge scale and high-dimensional heath mind data, the customary anomaly discovery techniques are not productive. Research article proposes a novel half breed anomaly identification strategy, to be specific, Pruning-based K-Nearest Neighbor (PB-KNN), which coordinates the thickness based, bunch based strategies and KNN calculation to direct successful exception recognition. The proposed PB-KNN receives the case arrangement quality character (CCQC) as the restorative quality assessment model and uses the characteristic covering rate (AOR) calculation for data order and dimensionality diminishment. To assess the execution of the pruning tasks in PB-KNN, numerous broad analyses have been conveyed out. The investigation comes about demonstrate that the PB-KNN technique outflanks the k-closest neighbor (KNN) and nearby anomaly factor (LOF) as far as the exactness and proficiency. Health-mind investigation represents a predictable test to doctors and is the territory of research in which trillions of sums are being spent by all nations. The Institute of Medicine proposes a few proposals to build the nature of health mind. Distinctive data mining calculations have been connected on the voluminous health records to help basic

Performance improvement in contemporary health care using IoT allied with big data 115 leadership process. The understanding health conditions are broke down and correct sickness is determined utilizing big data investigation to have evidence-based strategy. Patient’s medication history and current problems are considered to analyze and medicate the proposal. Quiet examination report is produced to screen the recuperation and patient input of the recommended medicate is recorded to witness the achievement rate of the analysis procedure. AEGLE enterprise focuses to construct an imaginative ICT arrangement tending to the entire data esteem chain for health in view of distributed computing empowering dynamic asset portion, HPC frameworks for computational speeding up, and propelled perception techniques. Analysis of the tended to big data health situations and the key empowering advances, and data security and administrative issues has been completed which are to be incorporated into AEGLE’s bioanalytical community, empowering propelled health-mind expository administrations, while additionally advancing related research exercises. Concerning system making exercises and the money related cost of air pollution, in, unmistakable approaches to manage dealing with the PM2.5 defilement issue are broken down. The makers assess for the present and future the satisfaction of EU and WHO air quality benchmarks for PM2.5 and gage the loss of life trust under different course of action circumstances. The money related cost of the general prosperity impacts of encompassing and family air pollution is surveyed, with particular reference to the countries of the WHO European Region. In light of the gave information, beginning at 2010, the yearly money related cost of surprising misfortunes from air pollution over the countries of the WHO European Region stayed at USD 1.431 trillion. Concerning the air sullying observing stations, there is a rising example for foundation of stations in a couple of countries and urban areas around the globe, giving thusly the potential for the obtaining of more information later on. Toward this heading and as communicated in, in a couple of countries insignificant exertion checking devices are being delivered to evaluate air tainting levels and exposures. Mobile phone applications, for example, empower customers to check air quality and look at continuous information on real outside sullying, while there are also a work in advance online stages for landings of consistent observing of information [27].

1.10 Health care monitoring frameworks As health care in modern communication technology has been made secured using advanced security technology, a still advanced cloud computing platform is currently being used for easy access of health information and better control with new encryption method. The encoded information will be secured in the cloud using provable information possession plot. To improve the assurance and security, the primal based security

116 Chapter 4 technique is used as a piece of securing the social protection information. The proposed framework gives better security. Using this proposed procedure, the exactness of therapeutic administrations information is ensured and conspicuous confirmation of archive piece nearness in the cloud is performed. This procedure furthermore reinforces incorporation, alteration and cancellation of the information and tries to diminish the calculation time of the server. The surprise attack are made with encoded information and the results show that the proposed plan beats with improved security in information accumulating when appeared differently in relation to the present methods. Health care monitoring frameworks are logically advancing with the possibility to change the way social insurance is as of now conveyed. A Smart Health Monitoring System, for example, the one composed in this investigation, means to computerize understanding monitoring errands. In the present investigation we have composed and built up a GSM (Global System for Mobile) based brilliant wearable framework with 3-hub Accelerometer, 3-lead ECG recording framework and constant NIBP examination framework. The gadget is fit for distinguishing sudden fall situations, heart abnormalities and hyper/hypotension; in this way rendering it suitable for constant monitoring, self-conclusion, and remote finding purposes. The framework is likewise fit for sending notices to social insurance experts quickly after the discovery of any of the previously mentioned health care abnormalities with the goal that the vital advances are taken. The total examination and assessment of the signs procured from the patient have been performed utilizing a product stage: NI LabVIEW. It is a savvy, keen and wearable health care monitoring framework that is of monstrous significance not exclusively to the elderly and postagent patients, yet in addition to those dwelling in remote areas and are unequipped for bearing costly social insurance in urban hospitals or facilities. Modern demonstrations are expressive of the way that the ordinary age of the human masses has extended through and through and the most copious purposes behind death are heart issue and issues of the cardiovascular structure. Starting now and into the foreseeable future, the necessity for reasonable preventive cardiology is being pushed by a couple of researchers. The most basic piece of intense preventive cardiology is to accumulate, screen and separate the prosperity information of individuals. Prosperity Monitoring and interpretation of the information from now on secured have reliably been the endeavors expectedly distributed to arranged therapeutic care staff. Despite the way that being more careful in the related data, the objectives to work are in like manner uncommonly undeniable. Fatigue factors and overwhelming workloads are both possible causes to delayed emergency response that may have diminished the chances for patients’ survival. By means of modernizing this strategy, the system frees the therapeutic specialists of the dull assignments to concentrate their contemplations on something significantly additionally asking.

Performance improvement in contemporary health care using IoT allied with big data 117

2. Conclusion With the improvement of different technical dimensions of problem solving, there are more prominent change in way of life of individuals in metropolitan city and simultaneously there is chance of more medical attention in smart cities. Due to use of more electronic devices and automatic systems, the radio range also penetrates to the body of human beings which has a great negative effect in their health system. Therefore to reduce the adversity of technology in health care, many research proposals are offered by scientists. Some of them use prevention methods using IoT and Big data analytics to build immune systems and to reduce the level of insecurity. Further by using Artificial Intelligence and Soft computing methods the systems are being handled more intelligently and logically so that the diagnosis can be done in better way with reduced delay. The above chapter presents an analytical study of appreciation of big data, their analytic process and IoT based systems with application process and challenges.

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118 Chapter 4 [10] M.I. Pramanik, R.Y.K. Lau, H. Demirkan, M.A.K. Azad, Smart health: big data enabled health paradigm within smart cities, Expert Systems with Applications 87 (2017) 370e383. ISSN 0957-4174, https://doi. org/10.1016/j.eswa.2017.06.027. [11] T.J. Carney, A.Y. Kong, Leveraging health informatics to foster a smart systems response to health disparities and health equity challenges, Journal of Biomedical Informatics 68 (2017) 184e189. ISSN 1532-0464. [12] A. Hussain, W.enbi Rao, Aristides Lopes da Silva, Muhammad Nadher, Muhammad Mudhish, Health and emergency-care platform for the elderly and disabled people in the Smart City, Journal of Systems and Software 110 (2015) 253e263. ISSN 0164-1212. [13] F.T. Zuhra, K. Abu Bakar, A. Ahmed, M. Ali Tunio, Routing protocols in wireless body sensor networks: a comprehensive survey, Journal of Network and Computer Applications 99 (2017) 73e97. ISSN 1084-8045. [14] R.K. Lomotey, J. Pry, S. Sriramoju, Wearable IoT data stream traceability in a distributed health information system, Pervasive and Mobile Computing 40 (2017) 692e707. ISSN 1574-1192. [15] A. Harbouche, N. Djedi, M. Erradi, J. Ben-Othman, A. Kobbane, Model driven flexible design of a wireless body sensor network for health monitoring, Computer Networks 129 (Part 2) (2017) 548e571. ISSN 1389-1286, https://doi.org/10.1016/j.comnet.2017.06.014, http://www.sciencedirect.com/science/ article/pii/S138912861730258X. [16] B.M. Knoppers, A.M. Thorogood, Ethics and big data in health, Current Opinion in Systems Biology 4 (2017) 53e57. ISSN 2452-3100, https://doi.org/10.1016/j.coisb.2017.07.001. [17] D. Finlay, Chapter seven e connected health approaches to wound monitoring, in: J. Davis, A. McLister, J. Cundell, D. Finlay (Eds.), Smart Bandage Technologies, Academic Press, 2016, ISBN 9780128037621, pp. 229e244. [17a] W.A. Rogers, T.L. Mitzner, Envisioning the future for older adults: Autonomy, health, well-being, and social connectedness with technology support, Futures 87 (2017) 133e139. ISSN 0016-3287, https://doi. org/10.1016/j.futures.2016.07.002. [17b] J. Lloret, L. Parra, M. Taha, J. Toma´s, An architecture and protocol for smart continuous eHealth monitoring using 5G, Computer Networks 129 (2) (2017) 340e351. ISSN 1389-1286, https://doi.org/10. 1016/j.comnet.2017.05.018. [18] S. Neethirajan, S.K. Tuteja, S.-T. Huang, D. Kelton, Recent advancement in biosensors technology for animal and livestock health management, Biosensors and Bioelectronics 98 (2017) 398e407. ISSN 0956-5663. [19] M. Khalifa, I. Zabani, Utilizing health analytics in improving the performance of healthcare services: a case study on a tertiary care hospital, Journal of Infection and Public Health 9 (6) (2016) 757e765. ISSN 1876-0341. [19a] E. Fotopoulou, et al., Linked data analytics in interdisciplinary studies: The health impact of air pollution in urban areas, IEEE Access 4 (2016) 149e164. [20] J. Archenaa, E.A. Mary Anita, A survey of big data analytics in healthcare and government, Procedia Computer Science 50 (2015) 408e413, 1877-0509. [21] J. Lepeule, F. Laden, D. Dockery, J. Schwartz, Chronic exposure to fine particles and mortality: an extended follow-up of the Harvard six cities study from 1974 to 2009, Environmental Health Perspectives 120 (7) (2012) 965e970. [21a] S. Neethirajan, S.K. Tuteja, S.-T. Huang, D. Kelton, Recent advancement in biosensors technology for animal and livestock health management, Biosensors and Bioelectronics 98 (2017) 398e407. ISSN 0956-5663. [21b] T. Shah, et al., Remote health care cyber-physical system: quality of service (QoS) challenges and opportunities, IET Cyber-Physical Systems: Theory & Applications 1 (1) (2016) 40e48, https://doi.org/ 10.1049/iet-cps.2016.0023. [21c] V. Vippalapalli, S. Ananthula, Internet of things (IoT) based smart health care system, in: 2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES), Paralakhemundi (2016) 1229e1233

Performance improvement in contemporary health care using IoT allied with big data 119 [22] P. Patel, M. Intizar Ali, A. Sheth, On using the intelligent edge for IoT analytics, IEEE Intelligent Systems 32 (5) (2017) 64e69. [22a] B.M. Knoppers, A.M. Thorogood, Ethics and big data in health, Current Opinion in Systems Biology, 4, 2017, pp. 53e57. ISSN 2452-3100, https://doi.org/10.1016/j.coisb.2017.07.001. [23] F.T. Zuhra, K.A. Bakar, A. Ahmed, M.A. Tunio, Routing protocols in wireless body sensor networks: A comprehensive survey, Journal of Network and Computer Applications 99 (2017) 73e97. ISSN 10848045. [24] R.K. Lomotey, P. Joseph, S. Sriramoju, Wearable IoT data stream traceability in a distributed health information system, Pervasive and Mobile Computing, 40, 2017, pp. 692e707. ISSN 1574-1192. [25] V.K. Solanki, M. Venkatesan, S. Katiyar, Conceptual model for smart cities: irrigation and highway lamps using IoT, IJIMAI 4 (3) (2017) 28e33. [26] V.K. Solanki, V. Muthusamy, S. Katiyar, Think home: a smart home as digital ecosystem, Circuits and Systems 7 (08) (2016). [27] M. Rath, V.K. Solanki, “Contribution of IoT and Big Data in Modern Health Care Applications in Smart City”, Handbook of IoT and Big Data, CRC Press, p. 109e122, ISBN 9780429053290 - CAT# KE71438.

CHAPTER 5

Emerging trends in IoT and big data analytics for biomedical and health care technologies Amit Banerjee1, Chinmay Chakraborty2, Anand Kumar3, Debabrata Biswas4 Microelectronic Technologies & Devices, Department of Electrical and Computer Engineering, National University of Singapore, Singapore; 2Electronics and Communication Engineering, Birla Institute of Technology, Mesra, Jharkhand, India; 3School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India; 4NUS-HUJ-CREATE Molecular Mechanism of Inflammation and Disease, Department of Microbiology and Immunology, National University of Singapore, Singapore 1

Chapter Outline 1. Introduction 122 2. Big data workflow for biomedical image analysis 124 3. Role of artificial intelligence and robotics in telemedicine 3.1 3.2 3.3 3.4 3.5 3.6

Robotics in health care 128 History of robotics 129 Tele-surgery/remote surgery 131 Applications 132 Artificial intelligence (AI) 132 Internet of Robotic Things (IoRT)

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133

4. Wearable devices and IoT 134 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8

Classification and categories of wearable devices 135 Communication modes of wearable devices in IoT 135 Very short distance 136 Short distance 136 Long distance communication 136 Working principles of wearable devices in IoT 136 Applications of wearable devices in IoT 137 Research challenges and open issues 138

5. Biotechnological advances 139 5.1 Neuroscience and brain research 141 5.2 Gene therapy 143 5.3 Big data enhancing stem cell research and tissue engineering

144

Handbook of Data Science Approaches for Biomedical Engineering. https://doi.org/10.1016/B978-0-12-818318-2.00005-2 Copyright © 2020 Elsevier Inc. All rights reserved.

121

122 Chapter 5 5.4 Big data of nanotechnology to nanomedicine 145 5.5 New drug discovery and drug delivery systems 147

6. Conclusion 149 References 149

1. Introduction This book chapter deals with the emerging advances in Internet of Things (IoT) and big data analytics for biomedical and health care technologies taking into account the cutting age research work carried out globally. IoT and big data analytics are expected to revolutionize many fields including biomedical and health care technologies as we know it today. This title would focus on recent advances and different research issues in biomedical technology and would also seek out theoretical, methodological, wellestablished and validated empirical work dealing with these different topics. The title covers a very vast audience from basic science to engineering and technology experts and learners. This serves common public interest by presenting new methods for the medical data evaluation, and diagnosis of different diseases to improve the quality of life in general, with a better integration into society. The contents on emerging trends in IoT and big data analytics for the following trends are highlighted with examples. Advanced Medical Imaging: Medical imaging currently employs from simple 2-D X-rays, ultrasound, CT scans, magnetic resonance imaging (MRI), and a host of other technologies, but researchers are developing new and improved imaging options like various forms of EM imaging, Terahertz or infrared imagining, thermography, combined with medical virtual reality, which would provide more accuracy and better outcomes in image-guided surgery, improved cardiac and lung imaging, giving physicians real-time and accurate views. Various detection and evaluation tools are available with the help of IoT and big data and are discussed in detail. Telemedicine: Telemedicine enhances the technological growth tremendously in various areas including informatics, medicine, materials science, telecommunications, artificial intelligence, virtual presence, computer engineering, and robotics. Telemedicine system is enhancing the health care performance by cost reduction, using artificial intelligence. The main goal of this chapter is to explore the significant role of IoT, artificial intelligence and big data in health care technology. This section mainly focuses on the four major applications: remote patient monitoring, medical information technology, medical data analysis, and accurate diagnosis with proper decision-making technology. Here telerobotic surgery and the importance of IoRT have been discussed with recent literature.

Emerging trends in IoT and big data analytics 123 Wearable Devices and IoT: A wave of wearable devices targeting the biomedical and health care applications far beyond tracking your steps each day has emerged. These devices are capable of collecting detailed information about our health, while also serving as a smartwatch or simple attachment combined with our mobile phones. The data collected and analyzed in standard protocols by machine intelligence looking for possible predictions of health-related issues. Beyond these prosthetic technologies combined with the advanced materials, design, computing and robotics has made enormous progress and now chip-based or auxiliary motors or robotics devices based assistive systems are possible. Biotechnological Advances: Various biomedical advances are extremely prominent and may transform the way we look at health care. Newer ways of gene treatment, nanomedicine, and nanoparticles used in selective drugs delivery or other substances to particular types of cells, like cancer cells, designed to be attracted to diseased cells, could be used as a way to deliver chemotherapy. Also, brain research is the hottest trend in biomedical engineering, including “neuro,” “transcranial,” and “EEG,” appearing in research paper titles more often on methods of restoring brain function after brain disease using different forms of stimulation, while others are working on the neural technology to power prosthetics. In general, IoT and big data analytics are two major technologies which will transform the biomedical and health care industry and make human life better. Big data applies to health care [1e4], agriculture [5,6], and social networking [7], among others. The core of big data analytics is based on (1) volume of data generated, (2) variety of data received i.e., different categories along with (3) data formation speed [2,8], (4) variability, i.e., the date irregularity; (5) partial inferiority in some data [9,10]. Considering these five variables and many additional factors, case basis, and big data analytics could be quite complex. For biomedical and health care, big data analytics can process complex and large datasets generated extremely fast in a meaningful manner so that health care or diagnostic service providers can interpret with existing tools [1,9]. At modern-day clinical system, where everything is computerized, biomedical and health care related data is generated in an unprecedented rate from various sources. The sources of most of the biomedical and health care data are largely clinical data, images and records uploaded from various sensors in centralized systems from, operations, pharmaceutical data etc. [11]. There are several interesting reviews on big data for health care that may be referred to here. Andreu-Perez et al. [1] in the review work on big data for health, dealt with the emerging trends in big data in biomedical, health care in terms of informatics: translational bioinformatics, sensor informatics, imaging informatics as an integrated approach. The report mainly focuses on aspects information management from a diverse range of data sources, which is both structured and unstructured, e.g., genomics, proteomics, metabolomics, imaging, clinical diagnosis, physiological sensing, etc., and its application

124 Chapter 5 in disease management by diagnosis, prevention, and personalized treatment. It is important that the widespread application of big data will bring in challenges in data privacy, security, and governance. Further, the idea of personalized health care with big data analytics to produce robust and effective for medical solutions are discussed by Viceconti et al. [4]. The report deals with the aspects of managing sensitive data; analytics of complex, heterogeneous data spaces; security constraints in distributed data management; and specialized analytics to integrate bioinformatics in clinical observations at tissue, organ, and organism scales. A detailed survey and broader view on the extreme methodological encounters confronted in an active big data setting is documented by Benjelloun et al. [11]. In the survey, it characterizes Hadoop framework along with several foremost components built in this framework related to (1) data storage, (2) integration, (3) processing and (4) interactive querying, with relevant examples. In general, developing big data applications with information availed from bulk datasets in traditional data acquisition methods seems not very competent due to sluggish sensitivity, the absence of scalable models and accurateness. Hence various distributions and technologies to implement the correct blend of diverse big data machineries for various specific applications and requirements in terms of diverse structure layers (e.g., storage, processing, querying, access and management) for advantages, limitations then final reallife usages is the need of the day for widespread use of the technology in day-to-day life.

2. Big data workflow for biomedical image analysis Among various types of biomedical and diagnostic imaging, the interesting once which deal with high speed, high definition and large size images, are X-ray imaging, MRI, ultrasonography (USG), photo-acoustic imaging, fluoroscopy, molecular imaging, computed tomography, positron emission tomography-computed tomography (PET-CT) [3]. The image database with advanced analysis may help in remote diagnostics e.g., radiological services, online image analysis, etc., which helps doctors diagnose critical illness even without physically traveling to remote places. Combined with machine learning (ML) algorithms that can study and develop sophisticated simulations, improvement of biomedical, and health care systems is obvious. Millions of images generated every day, which is helping various forms of artificial intelligence (AI) to create newer frontiers in big data analytics. The ML-based algorithms primarily cleanse structured information from crude datasets, translating them into expectations and assumptions to help adopt instantaneous actions [12]. An interesting report on the ideal processing and technique in big data algorithm for health care image processing is described by Kouanou et al. [13]. Their proposed workflow contains (1) attainment of health care data (image from various diagnostic systems), (2) inspection, (3) storing, (4) processing, (5) querying, (6) classification, and (7)

Emerging trends in IoT and big data analytics 125 programmed identification of health care images. The significance of using compressed health care data (i.e., images) in big data construction is described while comparing two main big data architectures based on (1) MapReduce in Hadoop and (2) Spark. They have proposed Spark architecture permits the development of suitable well-organized approaches that can handle the huge quantity of dataset (e.g., health care images) used for cataloging for personalized reference to one with other. It is complete, easier, adaptable and also facilitates the implementation of algorithms with its embedded libraries and provides a well-organized construction for health care dataset investigation. A broad overview of the construction of ideal approaches in big data analytics to automate biomedical image diagnosis is shown (Fig. 5.1). Further, Fig. 5.2 gives the common big data construction to program health care image processing where the classification stage used Hadoop or Spark to cluster images by set classification (details may be found in Kouanou et al. [13]). The proposed workflow supports the exchange of datasets, i.e., images just like conventional systems and can manage data from acquisition for storage and sharing of images. Another case study by Istephan and Siadat [14] on unstructured health care datasets (diagnostics images) query examines the viability of big data framework to deliver wellorganized querying in case of epilepsy disease diagnostic. Big data assesses a query in following stages: (1) organized information is employed to screen the unstructured database (2) with the use of Hadoop completing the query feature removal units are implemented on the datasets in a disseminated way. The details of the method may be found in Ref. [14]. The schematic of the proposed framework is shown in Fig. 5.3. The framework found to be useful for user-defined modules to deliver unrestricted means to

Figure 5.1 The flowchart for big data process for an example of health care image analysis in which the classification segment is compared with Hadoop or Spark structure design in the current report. Reproduced with permission from A. Tchagna Kouanou, D. Tchiotsop, R. Kengne, D. Tansaa Zephirin, N.M. Adele Armele, R. Tchinda, An optimal big data workflow for biomedical image analysis, Informatics in Medicine Unlocked 11 (2018) 68e74.

126 Chapter 5 (A)

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DATA image from instruments and connected systems

MRI

SMARTPHONE

Processing Image using Spark or Hadoop

Supervise Biomedical image analysis

(B)

Raw Data

Hadoop or Spark framework

Image by category

Run classification algorithm on Spark or on Hadoop

Figure 5.2 (A) Common big data construction to computerize health care image processing; (B) classification stage construction used to accumulation images by groups using Hadoop or Spark structure design. Reproduced with permission from A. Tchagna Kouanou, D. Tchiotsop, R. Kengne, D. Tansaa Zephirin, N.M. Adele Armele, R. Tchinda, An optimal big data workflow for biomedical image analysis, Informatics in Medicine Unlocked 11 (2018) 68e74.

analyze the unstructured health care datasets, covering the utilization of this framework outside epilepsy arena. Further researches for feature extraction modules are necessary for the application of the model in other medical domains which can help in the advancement of data-focused medication by utilizing the unstructured health care datasets. Another study on the classification of radiology datasets by big data, by Kansagra et al. [15] describes the implementation sequence that helps promote big data development of

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Figure 5.3 Projected outline to maintenance unrestricted query of indistinct data. Reproduced with permission from S. Istephan, M.-R. Siadat, Unstructured medical image query using big data e an epilepsy case study, Journal of Biomedical Informatics 59 (2016), 218e226.

academic radiology departments. This is because of current set-up of archived digital health care data, available with various radiology sectors are well situated for participation in the emergent big data analytics for a speedy influence on health care. Big data for radiology enables tailored image clarification, detection of any novel imaging indicators, significance calculation, and workflow categorization (Fig. 5.4). The big data analytics for the available data with the laboratories can help to find links in various health care parameters, which will enable easy and efficient diagnosis and suggest patients the right direction of treatment (Fig. 5.5) saving time, cost, and valuable lives.

3. Role of artificial intelligence and robotics in telemedicine Robotic technology, machine learning, AI, and swarm technologies will offer the future development of the Internet of things applications. Today, the most emerging Internet of Robotic Things (IoRT) brings new convergence issues like programmability and the communication between various mobile/robotic things for better coordination, exchange of information, security, configuration, and protection. AI is to revolutionize all industries, but perhaps none so much as health care. Instead of looking at single molecules, analyzing how entire networks of molecules work together to create a system and to combine this knowledge with other disciplines to gain a better understanding and compare with available databases and use that info to develop more effective treatments. Both biomedicine and ML could analyze data availed in national health databases to identify potential complications, as well as effective protocols, using the intelligence gained via data. Telemedicine utilizes information and telecommunications technology to transfer medical information for diagnosis, therapy, management, remote consultation and

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Figure 5.4 Discovery imaging indicators by various sources of big data. A comprehensive evaluation of wideranging experimental (i.e., clinical) data can decipher unfamiliar connections among various parameters. Reproduced with permission from A.P. Kansagra, J.Y. John-Paul, A.R. Chatterjee, L. Leon, D.S. Chow, A.B. Prater, J. Yeh, A.M. Doshi, C.M. Hawkins, M.E.H. Stacy, E. Smith, M. Oselkin, P. Gupta, S. Ali, Big data and the future of radiology informatics, Academic Radiology 23 (1) (2016) 30e42.

education [16]. The most important types are as follows: (1) Tele-consultation: between multiple carers without patient involvement via real-time or store-and-forward mode; (2) tele-education: between nonexpert, patient to Internet expert; (3) telemonitoring: transfer clinical information; and (4) telesurgery: assistance for surgery given via video respectively i.e., ROBOTIC arms is used to carry out remote surgical stages with good accuracy. Fig. 5.6 depicts the various applications of telemedicine for health monitoring.

3.1 Robotics in health care The robotic technology has been widely applied in health care that resolves several health issues. This technology is more superior in surgery than any other. The major features of

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Figure 5.5 Example of health care diagnostic chain, which covers testing (finding of a condition)dresult (recommend substitute analysis)daction (recognize supplementary diagnosis)doutcome in term of directing patients to altered treatment pathway. Reproduced with permission from A.P. Kansagra, J.Y. John-Paul, A.R. Chatterjee, L. Leon, D.S. Chow, A.B. Prater, J. Yeh, A.M. Doshi, C.M. Hawkins, M.E.H. Stacy, E. Smith, M. Oselkin, P. Gupta, S. Ali, Big data and the future of radiology informatics, Academic Radiology 23 (1) (2016) 30e42.

the robotic technology are as follows: (1) mobility, (2) programmability, (3) sensors, and (4) flexibility, which can operate using a range of programs, manipulates, and transport materials in a variety of ways. Fig. 5.7 presents the principles of the robotic technology.

3.2 History of robotics Machines become human assistants for a long time. The robot is the best example of machine devices which is applied to minimize risks, burden and gives better surgical outcomes with low cost. The history of robotic technology [18] has been discussed in this section. • • • • •

1921, 1985, 1987, 1992, 1993,

Robot Karel Capek introduced First medical usedPuma 560 neurosurgical biopsies First laparoscopic colon surgery was performed ROBODOC with IBM developed a robot for joint replacement First AESOP endoscopic robot to receive FDA approval

Disease Management

Data Interpretations

Health Education

Telemedicine

Disaster Management

Remote Monitoring

Critical care Monitoring Home Care

Surgery Robotics

Figure 5.6 Role of telemedicine in various applications [17].

130 Chapter 5 Robotic Technology

Act

Industrial (without human interaction)

Sense

Connect & monitoring

Think

Collaboration

Figure 5.7 Characteristics of the robotic approach.

• • • • •

1998, Dr. Friedrich-Wilhelm Mohr uses a robot to assist in the first heart bypass 2003, Da-Vinci receives FDA approval for robotic laparoscopic surgery 2008, ROBODOC receives 510(k) FDA approval for their THA robot 2010, First truly robotic surgery performed 2016, IoRT

The robotic technology in medical domain plays an important role to make a good coordination between expert doctors and patients. It provides unique synergy between medicine, health science, and many subdisciplines of engineering. Robotic technology provides good diagnostics, maintains electronic medical records (EMRs) and medical history preservations, and digital imaging technologies. The main objectives of this section are to understand the current state of robotics in health care and transitioning between electronic records requires enhanced reliable security. Robotic technology enhances the health care systems via real-time access where large volumes of data processed through a reliable transmission path like patient X-rays or digital scans. Lars et al. [19] design an inexpensive robotic tool i.e., Sister Kenny Home Therapy system (SKOTEE), that provides adherence support for home exercises, medication, reminders, and communication between doctors. The home-based robotic tool gives better performance with the help of different wearable and wireless body area network sensors for health monitoring. Clinicians can easily monitor and prescribe multiple patients simultaneously who belong to different geographical locations. Anett et al. [20] improved movement predictions from measurements of brain activity i.e., electroencephalogram (EEG) using robotic technology that provides more spontaneous man-machine communication. Yepes et al. [21] implemented an Android-based mobile application (Asimov) for remote control of a KUKA KR6 (industrial robotic arm) via WiFi. The different instruments can be used for robotic-assisted health care technology that shown in Fig. 5.8.

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Figure 5.8 Instruments for robotic health care [18].

3.3 Tele-surgery/remote surgery The surgery has been done by robotic technology under remote control by a human operator. The doctor gives surgical procedure and treatment modalities to the remote patient. The robotic platform (Da-Vinci) is developed that allowed surgery at a distance from bed region robot and patient in operation theater. It permits the doctors to operate surgery in other room/building and also provides expert advice via teleconferencing, teleradiology, and telepathology. Ashrafian et al. [22] mentioned the five important emerging dimensions for robotic surgeries are as follows: (1) technology: improves precision of surgery and provides good results, (2) cost, (3) evidence: helps best robotic operation, (4) training: large datasets improves the system accuracy; and (5) Awareness: requires for patients as well as society respectively. Ayush et al. [23] mentioned the various surgical robotic platforms like (1) ROBODOC, (2) ORTHODOC, (3) DA-VINCI and ZEUS, (4) ACROBOT, (5) PROBOT, (6) Minerva, (7) Pathfinder, (8) Renaissance, (9) RIO robotic arm, (10) iBlock, (11) Navio PFS, (12) Stanmore Sculptor, (13) AESOP, (14) Telelap ALF-X, (15) InnoMotion, (16) Sensei X, (17) Niobe, (18) CyberKnife, (19) Novalis with TrueBeam STx, (20) AutoPulse, and (21) C-leg (Otto Bock), respectively.

132 Chapter 5 The lists of robot-assisted surgery can be categories as follows: (1) thoracic (chondroma, esophagectomy, IMA harvest, IMA takedown, lobectomy, nodulectomy, epicardial lead placement, thymectomy, tumor resection, lung biopsy), (2) urology (ureter harvest. donor nephrectomy, prostatectomy), (3) vascular (vena cava tumor, aortic abdominal aneurysm), (4) cardiac (atrial septal defect repair, totally endoscopic coronary artery bypass, mitral valve repair), (5) general (esophagectomy, adrenalectomy, cholecystectomy, nissen fundoplication gastric bypass, heller myotomy), (6) gynecology (hysterectomy, cystocele repair, tubal reanastomosis, tubal ligation) respectively. Fig. 5.9 presents the instruments for telesurgery.

3.4 Applications The various robotic applications are (1) surgical: robotic procedures, (2) physical therapy: range of motion, flexibility, (3) bionic prosthetics: replacement limbs, organs, (4) caregiver-patient interaction, (5) simulators: procedure planning, education, (6) pharmacy: compounding and dispensing, (7) logistics: materials handling, delivery respectively. Robotics is mostly contributing three important sections, i.e., surgeons, hospitals and patients respectively that shown in Table 5.1 [17]. The major limitation of robotics surgeries are as follows: (1) high purchase and maintenance cost, (2) requires a cultural shift in hospital, (3) increased space in procedure and operating rooms, storage, maintenance areas, (4) potential possibility of intraoperative mechanical failure, (5) steep learning curve, (6) lacks tactile and force feedback, (7) operation may take longer due to set-up, (8) no additional insurance to offset higher costs, and (9) AI in health care.

3.5 Artificial intelligence (AI) AI is used to enhance the capacity of a computer-enabled robotic system to transmit data and produce good results. Medical artificial intelligence is mainly concerned with the

Figure 5.9 Instruments for telesurgery [18].

Emerging trends in IoT and big data analytics 133 Table 5.1: Important areas covered by Robotic technology [17]. Surgeons Improved patient care Enable complex tasks True-life 3-D vision Enhanced dexterity Superior ergonomics Comfortable seated posture Less blood loss Smaller incisions Scalable motions Elimination of tremor

Hospitals Increased efficiency Potentially reduced costs Potential reduced litigation Marketing tool

Patients Shorter hospitalization Reduced pain Faster recovery times Smaller incisions, resulting in reduced risk of infection, blood loss & scarring Autologous donation not required (i.e., Pre-surgery personal blood donation)

construction of AI programs that perform diagnosis and make therapy recommendations. The AI system analyzes a decision support method for critical care and observes the level of acceptance by the health professionals. AI improves the patient communication and enhances the system capacity to process and store large amounts of data in an intelligent manner. AI plays a major role in health care that can collect the multiple technologies enabling machines to sense, comprehend, act, and learn, so they can perform administrative and clinical functions. AI can greatly enhance care delivery and also increases the interoperability between systems, patients-doctors end, and EMR vendors. According to Accenture analysis, when combined, key clinical health AI applications can potentially create $150 billion in annual savings for the US health care economy by 2026. The main challenges of telemedicine are to expand system capabilities and improve the procedures to solve specific problems. AI and telemedicine provide robust patient monitoring, medical information technology, intelligent assistance diagnosis, and medical data analysis respectively. The AI is broadly focused in many areas like (1) management information system (MIS), (2) data stream, (3) rule-based (expert) systems, (4) datamining, (5) neural networks, (6) machine learning, (7) case-based reasoning, (8) data visualization, and (9) telerobotics, respectively. AI is used for various applications such as image recognition and interpretation, diagnostic assistance, generating reminders and alerts, therapy planning, etc. AI provides fast and accurate diagnostics to reduce human errors, cost reduction, virtual presence, and telemedicine.

3.6 Internet of Robotic Things (IoRT) Robotic technology, autonomous functions, and IoT jointly provides a good outcome in terms of the Internet of Robotic Things (IoRT). IoRT is the most demanding framework where intelligent devices can acquire sensor data from patients and process to a cloud server in an intelligent way in terms of proper decision-making unit. It takes faster decisions, proper visualization, and data manipulation, data acquisition, analytics, and

134 Chapter 5 communication. Robotic technology is used to sense, data collection, movement, manipulation, mobility, and intelligence that improves the IoT services. ML integrated robotic technology provides better intelligence with IoT where several robots can get interconnected. The central processor-controlled robots via secure WiFi networks. AIbased IoT robotic technology to be associated with IoT applications for providing optimized outcomes over particular applications. The main feature of the IoT and Robotics is sense the data and interact with the networked enabled devices. This feature may be expanded and implemented in terms of “sense-as-a-service” among various applications. IoRT exchanges the data streams with real-time computation mode via internal communication [24]. Chen et al. [25] proposed a cloud-enabled pillow robot for emotion sense and communication between patients through sensor-robots. Al-Taee et al. [26] improved the satisfactory results through robot-enabled glucose sensor for diabetes patients. Telemedicine has found application in every discipline of medicine. AI has been providing continuous support for taking proper decisions in medical robotics. The robotic technologies concerns toward lack of evidence and cost. The self-powered robot-assisted surgery gives better geometric accuracy. Doctors can use surgical robots for observing soft-tissue model with 3D imagery. The limitation of robotic surgery is the lack of tactile feedback. Doctors are mostly dependent on haptic feedback, but robotic methods to do not permit it. Currently, robots are controlled by a human that is error-prone that why need to develop an automated AI-enabled robotic approach for better patient monitoring. Advanced intelligent microinstruments need to be designed that can offer accurate dimension during operation effectively and efficiently.

4. Wearable devices and IoT A wave of wearable devices targeting the biomedical and health care applications far beyond tracking your steps each day has emerged. These devices are capable of collecting detailed information about our health, while also serving as a smartwatch or simple attachment combined with our mobile phones. The data collected and analyzed in standard protocols by machine intelligence looking for possible predictions of health-related issues. Beyond these prosthetic technologies has already made significant strides in recent decades with the advances in materials and development. Chip-enabled prosthetics are on the horizon with more mobility and flexibility, or even auxiliary motors that can help provide additional strength and power, or additional robotic devices that will continue to blur the lines between therapeutic and assistive devices. The major goal of wearable devices is used to activity recognition and sensing what we are doing, in which location [27,28].

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4.1 Classification and categories of wearable devices Wearable devices will be classified based on requirement and usage. Some devices are used based on a doctor’s instruction because it may lead to serious issues if not done with proper medical intervention. For example, ingestible sensors, wearable injectors, and wearable insulin pumps require proper medical intervention, and with that experts can decide the proper dosage. On the other hand some wearable sensors without and intervention by experts for monitoring to various applications is represented by Seneviratne et al. in “A Survey of Wearable Devices and Challenges” [29]. The report gives detailed classification and categories of wearable devices, e.g., wrist-worn (smart watches, wristbands), head mounted (smart eyewear, headsets), ornaments (smart jewelry, rings and chains), e-textiles (smart garments, t-shirt and wears), e-patches (sensor patches and e-tatoo), sports and fitness (smart wearable sensors, smart bracelets), etc. (Fig. 5.10).

4.2 Communication modes of wearable devices in IoT Though the devices are limited in size a work on a small battery to maintain minimum power consumption, they have their own constraints to minimize power consumption like limited processing and storing and computing power, which leads to limited transmission power. Based on distance coverage we have classified the following:

Figure 5.10 Classification and categories of wearable devices.

136 Chapter 5 • • •

Very short distance Short distance Long distance

4.3 Very short distance The technology adapted for very short distance communication is near field communication (NFC), which needs very low power consumption for shorter distance communication to transfer a tiny amount of data just by touching two devices together within a 4 cm range. An example is a contactless payment system.

4.4 Short distance Bluetooth is suitable for short distance communication. With low cost and low power consumption, the devices are powered by a coin cell battery. This wearable device can transfer limited data to the range up to 100 m theoretically. A maximum of eight devices can pair in Bluetooth; one is acting as a master node and the other seven nodes act as a slave node. Because of the limited data transfer its not suitable for multimedia communication. An example of a Bluetooth device would be calls coming through a Bluetooth headset, which is connected remotely to the cell phone. ANT is another wireless network for short distance communication technology for the application of sports and fitness to monitor the heart rate in cycling. Some of the ANT devices are SensRcore [29]. The detailed operation of the Bluetooth and ANT may be found in “A Survey of Wearable Devices and Challenges” by Seneviratne et al. [29] (Fig. 5.11).

4.5 Long distance communication The application requires the data has to transmit to longer distance with manageable delay, then WiFi and cellular networks are the best options. The device has more transmission power, processing power, storage power than shorter communication models. These communication models transmit multimedia data like voice and video. The further detailed information is available may be found in “A Survey of Wearable Devices and Challenges” [28].

4.6 Working principles of wearable devices in IoT The working principles of wearable devices in IoT are consists of three layers: First Layer: This layer contains the wearable sensors deployed in the application for sensing data. As per our figure, we considered the health care application, where the temperature, the heartbeat is sensed and transmitted to the higher layer.

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Near Field Communication (NFC)

Very Short range

Bluetooth and ANT

Short Range

Communication modes of Wearable Devices based on distance coverage Long Range WIFI and Cellular Networks

Figure 5.11 Communication modes of wearable devices in IoT.

Second Layer: The actual raw data from sensors is collected processed in the Local network and transmitted to Cloud Services for remote accessing with the help of various wireless communication technology like Bluetooth, Wi-Fi, ZigBee, RFID. Third Layer or Cloud Service Layer: The wearable sensors are stored and accessed data from cloud service through communication technology. Also used to analyze data and monitored the application in the remote system [30,31].

4.7 Applications of wearable devices in IoT Smartwatch-Based Wearable EEG System for Driver Drowsiness Detection [32]. To avoid the major accidents in national highways due to drowsiness of drivers, a wireless EEG device contains Sensory input unit (SIU) and sensory processing unit (SPU). The sensed data is converted to digital data transmitted to smartwatch through Bluetooth. The smartwatch contains vibrate sensor. If the driver had drowsiness first it will give the early signal, if the driver is no response, it will vibrate wakeup the driver (Fig. 5.12). Wearable IoT data stream traceability in a distributed health information system [33]. This technology is used to map the device data to the users in the heterogeneous environment. A wearable health monitoring system for posttraumatic stress disorder [34]. Posttraumatic stress disorder or PTSD is because due to a shocking, traumatic event, it’s commonly affected for veterans. With the help of wearable sensors, the patients are a monitor for nightmare conditions. With the existing collected data will go for training through ML techniques come out with an optimized solution for patient monitoring and decision taking. A model for predicting user intention to use wearable IoT devices at the workplace [35].

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Figure 5.12 Architecture of wearable devices and IoT.

A novel wearable device for continuous, noninvasion blood pressure measurement [36]. A wearable device which incorporate sensor is developed to measure the daily blood pressure if any small volume of BP is detected, it will transmit the data and display on screen through the internet for remote patient monitoring. This data will go for further analysis and suggest medication, suggest the sports and diet control the BP normal. IoT-based intelligent fitness system [37]. Virtual-Blind-Road following-based Wearable navigation device for blind people [38].

4.8 Research challenges and open issues Apart from the challenges in constrained like size, power and storage wearable devices in IoT as some open challenges are listed out [39]. Standards: Several organization maintains their own standards for communication, for example, ETSI maintains for Machine to Machine [M2M] and RFID. Some technology is long distance communication and some for short. 6LowPAN is used for low capacity devices. We have some diversity in standards. There is no common standard for IoT and it’s not integrated. Scalability and Adaptability: Everyday a new type of wearable devices are adding into the IoT application, with that integrating of devices in a heterogeneous environment is not a simple task [40]. Naming and Addressing: Due to scalability, the identification of devices in the application and real-time scenario is a great challenge. Because we are a shortage of name and

Emerging trends in IoT and big data analytics 139 Addressing. We already move from IPv4 to IPv6, near future if 100 billion of devices is joined for application will be a great challenge. Traffic Management: The huge number of devices will generate a huge amount of data in terms of Gbps, managing those data is the crucial thing. Identification of data belongs to concern sensors in health care application is a very challenging task, because at any time server receiving [41]. Privacy: A confidential data and health-related data is collected and shared without the knowledge of the concerned person. Validating the received from the health care and other application is a challenging task. HMI: Human Machine Interaction, Interface for the user to wearable devices is still a challenging task.

5. Biotechnological advances Biomedical and health care industry is mainly aimed at improving human health and living condition through proper and correct diagnosis, treatment and disease prevention [42]. The health care industry has always generated large amounts of data, driven and motivated by patient care, record keeping, administrative auditing, and conforming to compliance and regulatory requirements [43]. A small estimate of data generation emphasizes the necessity of a new paradigm of data collection and processingdthe ProteomicsDB, covering 92% of the known human genes, has alone acquired 5.17 TB of data [2]. The Visible Human Project, which depends mainly on medical imaging techniques like MRI and CT scans, has already recorded 39 GB of female datasets. The rate of implementation of the electronic health record system has tripled in the US hospitals to about 42% between 2009 and 2012 due to the enactment of the HITECH Act [44]. All such kinds of datasets have started to provide opportunities for large data aggregation and analysis. Big data has provided that novel ecosystem which is transforming the case-based biomedical practice into a large-scale data-driven the field of research and therapeutics. Big data is providing the tools to extract meaningful knowledge from new and previously stored patient health, including disease biomarkers, laboratory reports, medical prescriptions, population demographics, clinical diagnosis, and government surveys [45]. Computer-guided diagnostic systems capable of analyzing such hugely diverse data types into a cohesive output demands the establishment of an exclusive structure which will be able to assimilate data specificity and variety (Fig. 5.13). Data inputs in the form of text, images, biosensors, video, and audios will be processed, visualized and categorized leading to the final output of the system in the form of patient diagnosis. Big data analytics can potentially lead the health care providers to make informed diagnosis

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Figure 5.13 Conceptualized framework of a computer-aided diagnostic system, where various types of inputs will be integrated and analyzed by a user-driven iterative process to reach an output in the form of a diagnosis. Reproduced with permission from O.N. Oliveira, T.T.A.T. Neves, F.V. Paulovich, M.C.F. de Oliveira, Where chemical sensors may assist in clinical diagnosis Exploring “big data”, Chemistry Letters 43 (11) (2014) 1672.

decisions based on the insight gained from clinical and other data Repository using sophisticated analytical technologies. The advent of big data in the medical technology has led to the development of the medical Internet of Things (IoT), which is grossly defined as the cross-linking of the data computing resources working in various medical institutions via the internet, permitting them to exchange data among them [46]. A vast repertoire of devices is already being employed in health care management that not only generate massive amounts of data at an enormous speed but is also reducing the overall management costs of the diseased and chronically ill patients. This will revolutionize the medical practice and patient-doctor relation in a hospital, where electronic devices feed the cloud database with all kinds of patient information and will only be accessible to authorized persons, including the patient. It is estimated that 40% of IoT-related technology will be dedicated to healthrelated applications more than any other field, by the year 2020 [46]. Surely along with

Emerging trends in IoT and big data analytics 141 the free dispersal of information and knowledge, will come the ethical question of privacy rights that will need to be answered before effective employment of big data through IoT. In the extremely important and rapidly evolving background of IoT, let us discuss and understand some of the major fields in biomedical science that have been reaping the benefits from the incorporation of big data analytics into bioscience.

5.1 Neuroscience and brain research Novel techniques in neuroscience research are generating huge quantities of data at a logarithmic rate unseen ever before [47e49]. Techniques like patch clamping, optogenetics, EEG and fMRI, are allowing generation of neuro-technical information at overwhelming speed toward the establishment of a sound and immense data bank (Fig. 5.14). The arrival of big data has allowed for the upgradation of the process of single technique addressing the isolated problem in a lone species, into a more collective and

Figure 5.14 The spatiotemporal realm of neuroscience depicting the available methods to examine the nervous system in 2014, as described by Sejnowski et al. Inset summarizes the methods available in 1988, highlighting the area lacking effective techniques as large gaps. EEG, electroencephalography; MEG, magnetoencephalography; PET, positron emission tomography; TMS, transcranial magnetic stimulation; VSD, voltage-sensitive dye; 2-DG (2-deoxyglucose). Reproduced with permission from T.J. Sejnowski, P.S. Churchland, J.A. Movshon, Putting big data to good use in neuroscience, Nature Neurosience 17 (11) (2014) 1440.

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Figure 5.15 Diffusion magnetic resonance imaging (MRI) is among the many data types that researchers are employing and still deciphering to implement in brain research. Reproduced with permission from E. Landhuis, Big brain, big data (technology feature), Nature 541 (2017) 559.

integrative data collection utilizing a repertoire of techniques dealing with several problems in multiple species simultaneously. This has promoted the development of high-throughput data-mining strategies and innovations, so much so that several global and collaborative undertakings across the globe are aiming to attain complete and wholesome simulation of the human brain within the next couple of decades (Fig. 5.15). The inevitable contribution of big data in the development of neuroscience research during the coming ages was reflected in the US administration’s declaration of the Brain Research Through Advancing Innovative Neurotechnologies (BRAIN) Initiative in 2013, even though there are the present ethical concerns over the use of “neurologization” of mental health information [49]. There is a growing demand to make the brain data banks, along with analytical tools and protocols, publicly and freely available, especially for its application in psychiatric diagnosis and treatments. Yet, many questions need to be answered before making big data work for neuroscience and brain research. The main problem lies in the fact that data are getting generated in individually isolated labs using only a single of the vast array of available techniques. For example, one lab strives to record the electrical signal from the neurons without recognizing the nature of the functioning neurons, while another characterizes neuronal connections in the brain without checking their electrical signaling. Also, different techniques are used to study the similar phenomenon, without any actual integration of the data obtained. Hence, standardization of obtained data and information forms a major requirement for big data to start contributing significantly to brain research. There are a few other minor hurdles that need to be breached before integrating big data into the foundations of neuronal research. Integration of models used to study different phenomena can be overcome using simulated neuronal networks. But for that to work the huge data set generated across the globe needed to be normalized to a global standard for the ease of

Emerging trends in IoT and big data analytics 143 analysis and application. What we need is a breed of a new generation of computer-literate researchers who can sort the rich and intricate knowledge collected from brain and neuroscience research laboratories into carefully simplified and tested theories and applications.

5.2 Gene therapy Visionary scientists predicted the techniques of gene modification to be an effective treatment strategy, especially for inheritance-linked human diseases, almost five decades ago [49]. Genetic modification, or gene therapy as it is known more recognizably, had then given the futuristic hope for a robust and lasting clinical curative that might be attained in a single treatment step. With the advent of big data analytics, however, gene therapies are fast becoming an essential and indispensable component of modern armaments in the therapeutic defense against an array of inherited and acquired human diseases [50,51]. This has been possible now with the help of the generation of powerful analytical tools, faster computing machines and upgradation of storage facilities that finally allow the integration of about 30 years of scientific, clinical and advanced manufacturing knowledge. A lot of challenges prevail, including elaboration and prevention of genotoxicity, optimizing gene transfer efficiencies toward effective treatment, preventing immuno-toxicity that limits in vivo applications and overcoming regulatory obstacle impeding enhanced manufacturing. In addition, there is always the everlasting difficulty in terms of obtaining proper funds for the development of such therapeutics and delivering them to the needy in an economically feasible format. However, the enduring benefits of a highly efficient disease combating approach provided by gene therapies, epitomized by the medical and scientific achievements from the last decade or so, inspires a sustained effort toward incorporating these therapies into the standard human treatment regime. Adeno-associated viral (AAV) and lentiviral vectors form the most recently approved line of techniques while gene editing therapies are still being in the state of experimentation with increasing success [50]. Lentiviruses and AAV have shown great promise allowing efficient and nontoxic gene transfer into human somatic cells. These vectors are showing increasing efficacies in the treatment of immune-deficient and cancer patients [52]. The full therapeutic exploitation of AAV vectors mandates extensive analysis of anti-AAV immune responses at both the cellular and humoral level [53]. In comparison to AAV and lentiviral’s capability of only gene addition, gene editing tools can allow various genetic manipulations of the target cells, including gene addition, rectification and deletion. And hence, the genetic editing approaches provide the hope toward a future of finding a permanent solution in organ-targeted patient treatments, without the necessary disadvantages of organ transplantation [54].

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5.3 Big data enhancing stem cell research and tissue engineering Stem cell research has revolutionized tremendously in the last decade or so. Technologies like human pluripotent stem cell (hPSC) development and CRISPR/Cas9 genome editing tools, have enlarged the capacity of the stem cell research field so much so, that an age of futuristic Biomimetic engineer providing patient-customized cells, tissues and organs toward patient-specific management and studies of countless diseases is not too far [55]. And this atypical designer focus of the field actually requires a set of data-based stem cell and tissue engineering methodologies completely distinct from other fields at multiple levels (Fig. 5.16). The first goal is to produce cells having identical transcriptional, epigenetic, and other phenotypic characteristics to that of intended targets [56]. The regenerated cellular components must possess equivalent phenotypic characters, proliferative capacity, 2D/3D spatiotemporal stratification or arrangement, and functional properties (for example the contraction of heart cells and the depolarization of neurons) similar to the tissues and organs to be replaced. According to Kawamata et al. multiomics strategies will provide necessary coordinating directives to generate precise definition of cellular identities, which will help engineer most fitted cells and establish quality criteria for efficient use of stem cellederived products for customized treatments and improving current criteria for their clinical evaluation [57].

Figure 5.16 The exclusively varied application of current stem cell research motivated by concurrent datadriven strategies including cell sample sizes, available kinds of data, their utilization, and the queries that demand solution to launch quantitatively adequate and sufficiently responsible regeneration of cells/tissues/organs for medicinal cure and treatments. Rreproduced with permission from A.D. Sol, H.J. Thiesen, J. Imitola, R.E.C. Salas, Big-data-driven stem cell science and tissue engineering: vision and unique opportunities, Cell Stem Cell 20 (2017) 157.

Emerging trends in IoT and big data analytics 145 In contrast to applications like in cancer science, where phenogenotypic data is used for precise identification of diseased cells and tissues as well as of new ways to eradicate them, the primary aim of stem cell research is to manipulate the same phenogenotypic information for generation of new cells and tissues closely resembling the desired cellular component [55]. To achieve the said purpose of efficiently and safely produce patientspecific cellular components, it is required to capitalize on the huge amount of phenogenotypic big data, obtained from the different fields of genomic, proteomic, metabolomic, transcriptomic, microscopic, and several other derivable fields of organic knowledge. Therefore, establishing a coordinating community to produce, regulate and control the free distribution of stem cell research data appears to be of vital importance to facilitate global collaboration and integration. Such a strategy had recently been implemented for the US cancer patient community in 2016 through the Cancer Moonshot Task Force report. To summarize, a precise plan for stem cell research motivated by quantitative big data generation, along with a parallel development of a strong clinical/ industry oriented computational resource will be pivotal to shape the quantitative, predictive, and therapeutic future of this application field.

5.4 Big data of nanotechnology to nanomedicine The word “nano” is derived from the Greek word for “dwarf” and thus able signify the one-billionth fraction of a meter, known as nanometre (nm) [57]. Nanotechnology is a rapidly increasing field of study of matter and phenomenon between the dimensions of 1e100 nm, as roughly described by the US National Nanotechnology Initiative (NNI) (Fig. 5.17). Nanotechnology thus allows the examination, management, and manipulation of the physical, chemical, mechanical, biological, and optical properties of matter at molecular, atomic and subatomic levels, using natural phenomenon like quantum effects

Figure 5.17 Depiction and comparison of the absolute sizes of nanoscale, microscopic, and macroscopic materials. Reproduced with permission from C.L. Ventola, The nanomedicine revolution: part 1: emerging concepts, Pharmacy and Therapeutics 37 (9) (2012) 512.

146 Chapter 5 [58]. It was estimated in 2014 that the total market value for nanotechnological products would be about $26 billion worldwide. This market for nanotechnology, with a compounded growth rate of 19.8% annually, is expected to grow and expand into an industry valued at about $64.2 billion from 2014 to 2019, as predicted by online market research reports [59]. Nanotechnology merged with medicine gives birth to the interdisciplinary field of nanomedicine. The National Institutes of Health (NIH) defined nanomedicine as the field of nanotechnology for the study of biology, with the aim of designing biomolecules that will possess roles different than their natural ones, and to use more specific chemical, synthetic, biological, molecular and biochemical methodologies for manipulating target biological systems [58]. Nanomedicine has always constituted on of the major field of major of applied nanotechnology. Recent sophisticated genetic, proteomic, molecular and cellular biological, material scientific, and bioengineering technology have all promoted the scaling down of the huge biological world of living organisms into a measurement parallel to the intricacies of nanotechnology (Fig. 5.16). As is evident, most inner living functions required for the survival of individual cell and the natural organism as a whole, happens in the magnitude of nanoscale, since the measurements of most biologically functional molecules like water, glucose, antibodies, proteins, enzymes, receptors, and hemoglobin exist at the microscopic dimensions. Many scientific endeavors, presently focused on biomedical therapeutics, instruments, and devices, are trying to implement enhanced efficiency, sensitivity, safety, and user customization using nanotechnology. Nanotherapeutics provide advantages including increased bioavailability, better dose response, decreased toxicity, and higher solubility compared to concurrent medications. The most commonly applied nanoparticles (NPs) include polymers, liposomes, iron oxide particles, quantum dots, and carbon nanotubes and nanoshells. Ranging from 90 to 150 nm in diameter is made of a lipid bilayer membrane and an empty core designed to carry biomolecules ligands conjugated to the liposomal surface. Another example of NP is carbon nanoshells which are primarily used in thermal ablation therapy. NPs can also be designed to release therapeutic or diagnostic materials only when activated in response to a specific site or stimulus. For example, the mildly acidic inflammatory environment present inside tumor endosomal vesicles and lysosomes (pH 4.5e6.0) can be trigger drug release from suitably synthesized and manufactured NPs. However, what is lacking currently in this field are the pharmacokinetics, pharmacodynamics, and toxicity data, for many nanomaterials which undermines the potential benefits of this latest arsenal of medical biology. And this is the area where big data analytics are getting applied to integrate and interpret the massive amount of data scattered around the scientific community [60]. Highly proficient computer-aided diagnosis systems with high-throughput computational methods and capabilities are the needs of the hour. Big data paradigm has brought hope that such systems will soon be available in routine clinical practices and

Emerging trends in IoT and big data analytics 147 specialized therapeutic applications. Since diagnosis involves a classification of the symptomatic data, the supervised and unsupervised or automated branches of ML are needed to be woven into a single application system to cope with this decision-making. Already the ultramodern and futuristic health care systems have started relying on advanced nanosensors and analytical methods, leading to the accumulation of large amounts of data which essentially require ML, big data organization and implementations. Building these computer-aided diagnosis systems call for a definitive merging of efforts from computer engineers, and physicists on one hand and on the other from various biological and life science professionals, health experts and chemists from different fields of medicine.

5.5 New drug discovery and drug delivery systems Drug development has been a long process deprived of major success, mostly due to the absence of understanding the varied degree of individual toxicity and response to drugs [61]. Increasing manufacturing costs and skyrocketing price further deterred the growth [62]. To counter these hurdles and meet the increasing medical demands, pharmaceutical companies are encouraging publiceprivate segment alliances, improved strategies, and utilization of big data to advance the ways of drug research and development. There are different magnitudes of publiceprivate collaborations providing differential scope and time for development to the involved parties. These ventures can be limited to projectspecific associations based on strategic alliances or can enlarge into multiparty industrial conglomerates offering varied opportunities and challenges. The most common and popular type of industrial collaborations is the one initiated by lab investigators. However, such efforts have their inherent ethical, legal and financial aspects that need to be taken care of. Adaptive trial projects, when meticulously designed and planned before implementation, can actually be equally advantageous. The alternative use of the big data strategy is finally giving a scope to translate current knowledge into utility information aimed at drug discovery and development. Recently developed computerized databases of gene/protein targets n diseases and available therapeutic chemicals, along with functional and clinical readouts, have been extremely instrumental toward the success of this novel approach. Further, this approach has accelerated the systematic high-throughput identification of novel drugs or helped realign existing targets for medicines countering the individual patient-specific pathogenic molecular aberrations. Booming innovations in data handling technology and direct-to-consumer diagnosis has further enabled big data usage to accomplish higher precision in customized medicine. However, respecting the boundaries of individual privacy and informed consent appears to be the key challenge for the use of big data. Among all the facets of drug development and application, the development of improvised novel drug delivery systems has benefited most from the implementation of big data computing and application [63,64]. Drug delivery systems

148 Chapter 5 (DDS) are conceived to release drugs at controlled rates or to a specific systemic target inside the body. This will eventually facilitate concentration of drug only in the target tissue, preventing the body from collateral effects. Thus, novel carrier particles like liposomes, nanoparticles, and microspheres offer a smart solution for drug delivery through linkage of the carrier particles to the drug of choice. This drug-carrier linkage strategy allows modulation of controlled drug release and the desired rate of absorption as per requirement [63]. Among the available systems at present, microspheres of various types are increasingly being used to control release and targeting the drugs to specific tissue sites (Fig. 5.18). These microspheres are equipped differently to achieve their functions, depending on their types (Fig. 5.18). Microspheres constitute the novel DDS that has benefited from the knowledge processing talent of big data. Microspheres are spherical particles between 0.1 and 200 mm in size, used not only for prolonged release but also for targeting of anticancer drugs to the tumor. Microspheres use various mode of action to accomplish a nontoxic, directed and functionally efficient delivery of drugs in vivo and transport of dietary supplements into the diseased organ and tissues of the body (Fig. 5.18). Bioadhesive microspheres are designed to adhere to available tissue surfaces in body organs like an eye, nasal cavity, urinary, colon, and gastrointestinal tract, thus allowing localized as well as the systemically controlled release of drugs. Magnetic microspheres can be filled with drugs or radioactive materials and targeted to specific sites of illnesses by applying a magnetic field to the patient body. Floating microspheres, mostly used in fluid-filled body cavities like blood vessels and stomach, remained buoyant in the cavity while slowly releasing the drug at the desired rate. Radio immobilization therapy microspheres and Mucoadhesive

Figure 5.18 Microscopic view of microspheres used in drug delivery and different modes of action employed in microspheres. Reproduced with permission from P. Agrawal, S. Rajput, A. Pathak, N. Shrivastava, S.S. Baghel, R.S. Baghel, Microspheres a magical novel drug delivery system: a review, World Journal of Pharmacy and Pharmaceutical Sciences 1 (1) (2012) 439.

Emerging trends in IoT and big data analytics 149 microspheres are used for targeting tumor and mucosal body surfaces, respectively. Big data is aiming to design combinations of diverse strategies that will lead to microspheres occupying a fundamental and vital domain toward formulating one of the most efficient delivery methods for drug and dietary supplements to diseased organ and tissues in the body, in the very near future.

6. Conclusion From the trends of the emerging applications of IoT and big data analytics in various fields discussed in this chapter, it is very much evident that the future of biomedical and health care technology will heavily count on the current research and methodologies developed for wide-scale application in real-life problems. The technologies discussed here are anticipated to be the main steam innovations and are already drawing enormous attention and funding from various public and private organizations. However, it may be noted that even with extensive and sincere efforts in selecting the fields to discuss here, considering the massive volume of technical reports available on various technological advancement in the field, the readers are encouraged to study further and seek out for deeper insights into the specific fields of their interest.[65,66]

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CHAPTER 6

Recent advances on big data analysis for malaria prediction and various diagnosis methodologies Salam Shuleenda Devi1, Vijender Kumar Solanki2, Rabul Hussain Laskar3 1

National Institute of Technology Mizoram, Aizawl, India; 2CMR Institute of Technology (Autonomous), Hyderabad, India; 3National Institute of Technology, Silchar, Assam, India

Chapter Outline 1. Introduction 153 2. Disease prediction model based on big data analysis 3. Diagnosis techniques 155 3.1 3.2 3.3 3.4 3.5

Clinical diagnosis 155 Manual microscopic examination of blood smear Quantitative buffy coat (QBC) 156 Rapid diagnostic test (RDT) 156 Computerized diagnosis 157 3.5.1 Database collection setup 157 3.5.2 Preprocessing of blood smear image 158 3.5.3 Segmentation 159 3.5.4 Microscopic feature extraction 164 3.5.5 Feature selection 168 3.5.6 Malaria infection identification 169 3.5.7 Computer-aided malaria diagnosis 172

154

155

4. Discussion 175 5. Conclusion 177 Acknowledgments 177 References 177

1. Introduction Malaria is a parasite disease induced by Plasmodium species and transmitted by Anopheles mosquito. It has different Plasmodium species, i.e., P. vivax (ring, schizont,

Handbook of Data Science Approaches for Biomedical Engineering. https://doi.org/10.1016/B978-0-12-818318-2.00006-4 Copyright © 2020 Elsevier Inc. All rights reserved.

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154 Chapter 6 gametocyte), P. falciparum (ring, schizont, gametocyte), P. malariae (ring, schizont, gametocyte), and P. ovale (ring, schizont, gametocyte).These Plasmodium species are further classified into trophozoite, schizont, and gametocyte life-cycle. Malaria is responsible for the death of the millions of people in Asia and Sub-Saharan Africa region [1]. Among those species, Plasmodium vivax and Plasmodium falciparum are commonly reported in India. The different life-cycles of the malaria parasites exhibit functional as well as morphological changes in the infected erythrocyte [2,3]. Different models have been developed to predict as well as to diagnose the disease. Prediction is mainly done based on the basis of big data analysis [4e7] whereas for diagnosis, techniques such as manual microscopic evaluation (MME), quantitative buffy coat (QBC), rapid diagnostic tests (RDTs), and computer assisted diagnosis systems have been reported for malaria screening. The manual microscopic examination remains the gold standard for malaria diagnosis. In manual evaluation, thin (morphology of blood components can be seen) and thick (for parasites detection in large volume) smears were used. However, manual microscopic examination is a tedious process and depends on the expert skill. To solve some of the limitations of manual examination, computer-assisted diagnosis systems based on digital image processing techniques has been developed by various researchers [8]. Microscopic image analysis plays a very important role to process and analyze the image acquired through microscope using computerized digital image processing (DIP) techniques [9e12]. For the analysis, the digital microscope imaging system has been used to capture and store the microscopic images. The system involves microscopy optical module, data acquisition, DIP module and software control module [13,14]. The system can be broadly classified into two types, i.e., erythrocyte classification and parasite segmentation. In erythrocyte classification system, erythrocyte has been classified into different classes sush as noninfected, and infected (with parasite life-cycles). It comprises of erythrocyte extraction (illumination correction, segmentation), feature extraction and classification. In parasite segmentation system, different models such as histogram thresholding, unsupervised classification, etc., have been used. It can be done by using both the thin and thick smear [15e19].

2. Disease prediction model based on big data analysis Big data is the trends which are used to analyze the extensive data with various techniques and preparing the data in human accessible format. In health care, big data analysis plays important role. It helps to analyze and predict the outbreak of various diseases. Different prediction model have been proposed to predict the disease based on the available data such as clinical property instances [4,5]. With the development of health care systems, extensive data are stored in different areas. With these, various health predictions can be made. From the past patient records available in hospitals, predictive analysis is performed to predict the disease using big data. Prediction model based on big data analysis are

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grown rapidly in recent research activities for health care [6]. Thakur [7] proposed a neural network malaria prediction model. The system is based on the primary health centers data collected from the department of vector borne diseases (DVBD), Khammam district. Data includes relative humidity, temperature, rainfall, vegetation for the period of 1995e2014. The result shows that the performance of prediction model varies with area to area and rainfall. With the help of clinical data (i.e., patients treatment records), the system performance got improved. Most efficient model is the prediction model using big data with environmental and clinical data.

3. Diagnosis techniques The accurate and timely diagnosis of malaria is needed to treat the patient and to prevent the further spread of the malaria infection. Malaria diagnosis involves malaria species and life-cycle stages identification in peripheral blood smears. Delays in diagnosis and treatment are leading causes of death in many countries. Various techniques, i.e., clinical diagnosis [9], MME [10], QBC [11], RDTs [12], and Computerized diagnosis are used to examine malaria as shown in Fig. 6.1.

3.1 Clinical diagnosis It is based on the patient’s symptoms found at the time of examination. The symptoms include fever, chills, sweats, headaches, muscle pains, nausea, and vomiting. As the symptoms are not specific to malaria, further confirmation is needed. So, the clinical findings should be confirmed by the MME of the peripheral blood smear [9].

3.2 Manual microscopic examination of blood smear Malaria is diagnosed by MME of peripheral blood smear by the well-trained expert. The blood smear is obtained by smearing finely on a glass slide and stained in such a way to allow various blood components to be examined through a microscope. Different staining methods such as Leishman, Giemsa and Jaswant Singh Bhattacharya (JSB) are used for staining the blood smear. Leishman stain has some advantages over others as it is a cheap

Diagnosis Techniques

Clinical Diagnosis

Manual Microscopic Examination

Quantitative Buffy Coat

Rapid Diagnostic Test

Figure 6.1 Various malaria diagnosis techniques.

Computerized Diagnosis

156 Chapter 6 Table 6.1: Comparison of the different smears. Findings Parasite presence Species identification Life-cycle identification Use

Thin smear Yes Yes Yes To identify the type of parasite

Thick smear Yes No No To identify the vicinity of parasite

and easily available. It is an easy staining technique which gives a fairly acceptable contrast between blood components in thin blood smears. The erythrocyte morphology is more visible with Leishman stained blood smear. Also, the time taken for staining is less. Moreover, the two different smears (thin and thick smear) are used for malaria detection. The comparative analysis of the thin smear and thick smear is shown in Table 6.1. From these, it has been observed that the thin smear is more effective as it permit malaria species identification. In thick film, large volume of blood can be observed, but the appearance of the parasite is much more distorted. The malaria species detection and classification using thin and thick smear remains the benchmark method. However, the manual evaluation performance depends on the staining quality and experience of the clinical expert. It has limited reliability and is time consuming.

3.3 Quantitative buffy coat (QBC) The QBC test is a newly developed microscopic method for identifying the malaria parasite’s presence in the peripheral blood smear [11]. It involves staining of the centrifuged and compressed erythrocyte layer with acridine orange, and examination is done under a ultraviolet (UV) light source. The test of QBC is as sensitive as the peripheral smear. However, the identification of the malaria species and quantification of parasitemia is difficult with QBC test. Although the QBC technique is simple, userfriendly and reliable, it is more costly in comparison to light microscopy and requires specialized instrumentation. So, it cannot be considered an alternative method for malaria diagnosis.

3.4 Rapid diagnostic test (RDT) An RDT is an alternate nonmicroscopic malaria diagnosis method by identifying the malaria antigens in the person’s blood. Testing procedure includes various steps: patient blood specimen collection; applying it on the test card along with the reagents. Further, specific bands present in the test card window shows the patient report within 15 min. The main advantage of the RDTs is less time consuming. However, the RDT may be unable to detect the infection with lower percent of parasites. Therefore, all the negative as well as positive RDTs must be followed by microscopic examination to further confirm the result [12].

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Table 6.2: Comparison of different diagnosis techniques. Manual microscopic examination Quantification Species identification Life-cycles identification

Yes Yes (gold standard) Yes (gold standard)

QBC Yes Yes Yes

RDT None None None

Computerized diagnosis Yes Yes Yes

3.5 Computerized diagnosis The computerized malaria diagnosis is a digital image processingebased microscopy diagnosis technique. It may be used to substitute the manual evaluation. It can also be utilized in various field such as research, treatments and clinical diagnosis. The advantages and disadvantages of the various diagnostic techniques in comparison to computerized diagnostic techniques have been discussed and shown in Table 6.2. From these, it is seen that the requirements of the computerized diagnosis would be similar to manual diagnosis with less time. However, the computerized diagnosis requires imaging equipment and a computer which can make it less accessible in rural areas. The analysis of the blood smear can be performed using the computerized diagnosis method within few minutes. The overall framework of the computerized diagnostic method can be developed by anlayzing the details of microscopy diagnosis and implementing it by using pattern recognition algorithms and digital imaging technique. The computerized diagnosis system has contributed in different levels, such as database collection, preprocessing of the blood smear, erythrocyte segmentation, feature extraction with selection, and classification. The various methodologies have also been developed to characterize the infected erythrocyte classification. 3.5.1 Database collection setup As the manual microscopic examination is done by the naked eye under the conventional optical microscope, it has limited reliability. It results in eye fatigue and also the image information cannot be stored for future analysis. To overcome this limitation, the digital microscopic imaging system has been developed. It is an integrated design with the combination of optical microscope, digital multimedia and digital processing technology [20]. The digital microscope imaging system of Cachar Cancer Hospital and Research Center used for microscopic image database collection [21,22] is shown in Fig. 6.2. The setup consists of four components, i.e., microscope digital module, data acquisition module, software control module, and digital image processing. Here, Imaging is done by using the microscopic digital module. The produced image is record and stored in the digital format with the help of data acquisition. Then, the computer storage device is used for digital images storing through universal serial bus interface (USB). This system controls the image capture and processing to improve the quality and further analysis of

158 Chapter 6

Figure 6.2 Database collection setup of Cachar Cancer Hospital and Research Center.

the images. Due to the various advantages of the digital microscope imaging, it has been used in various applications such as medical imaging for the diagnosis, etc. The main advantage of the digital microscope imaging system is that it can produce the images and store them in the storage device for the analysis. Using these images, the researchers can develop various image processing techniques to analyze the content of the images. It can also replace the tedious and time-consuming process of the manual microscopic examination. The microscopic images of blood smears can be captured using two different smear images, i.e., thin smear and thick smear. For computerized malaria diagnosis, various approaches can be done as shown in Fig. 6.3. 3.5.2 Preprocessing of blood smear image The purpose of the preprocessing is to enhance the image quality by background nonuniformity correction, noise reduction and contrast enhancement. The background nonuniformity, i.e., nonuniform illumination is mainly due to the staining variation and camera lighting effect while capturing the microscopic images of the blood smear. Staining of the blood smear is an important task to enhance the visualization of pathological disorder. The procedure of staining quality is fully dependent on clinical expert experience and perception. The variation in staining and mishandling of the slides introduce noise in the smear images. Different preprocessing techniques have been used to elevate the image quality for further processing and median filter is often used in several studies to reduce the noise in microscopic images as shown in Table 6.3. In most of the study, it has been observed that the illumination correction and noise reduction are the two important tasks for microscopic image preprocessing. A Gray world assumption is the commonly used illumination correction techniques for the microscopic images [23e29,34]. The techniques such as low pass filter, adaptive histogram equalization, Gaussian filter, Smallest Univalue Segment Assimilating Nucleus (SUSAN) filter, Laplacian filter, partial

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Figure 6.3 Malaria diagnosis steps with different techniques available in the literature.

contrast stretching, contrast stretching, hybrid median filter, morphological operations and local histogram equalization have also been used to improve the microscopic image quality of both thin and thick blood smear [50e64]. Das et al. [65,66] used the geometric mean filter to enhance the peripheral blood smear images. Somasekar et al. [67] used the combination of gamma equalization and adaptive median filter for illumination correction and noise reduction respectively, in thin blood smear images. Other techniques such as dark stretching and mean filter have also been used for preprocessing the microscopic images [68,69]. 3.5.3 Segmentation Image segmentation involve image partitioning into various region for the detail analysis [70,71]. For malaria detection, the analysis of erythrocyte has been done using two different approaches, i.e., some studies have segmented the whole erythrocytes whereas other segments only the infected erythrocytes or parasites for the analysis.

160 Chapter 6 Table 6.3: Preprocessing techniques for stained blood smear images. Literature Tek et al. [23e27], Springl [28], Das et al. [29], Devi et al. [30e33] Ruberto et al. [18], Ross et al. [34], Springl [28], Anggraini et al. [35], Berge et al. [36], Khan et al. [37], Edison et al. [38], Somasekar et al. [39], Das et al. [40,41], Ghosh et al. [42], Savkare et al. [43,44], Mehrjou et al. [45], Malihi et al. [46], Maysanjaya et al. [47], Ghosh et al. [48], Mushabe et al. [49], Devi et al. [30e32] Diaz et al. [50,51] Sio et al. [52], Zou et al. [53], Sriram et al. [54], Arco et al. [55] Leong et al. [56], Arco et al. [55] Soni et al. [57], Khan et al. [37], Ahirwar et al. [58], Patankar et al. [59] Ghosh et al. [42] Nasir et al. [60,61], Nanoti et al. [62] Maysanjaya et al. [47] Maity et al. [63] Mushabe et al. [49] Suradkar et al. [64] Das et al. [65,66] Somasekar et al. [67] Hanif et al. [68] Tsai et al. [69] Somasekar et al. [67], Devi et al. [33]

Staining

Preprocessing techniques

Giemsa/Leishman

Gray world assumption

Giemsa/Leishman

Median filter

Giemsa Giemsa/ Jaswant Singh Bhattacharya (JSB) e Giemsa

Low pass filter Adaptive histogram equalization Gaussian filter SUSAN filter

e Giemsa Giemsa Leishman Giemsa e Leishman Giemsa e e Giemsa/Leishman

Laplacian filter Partial contrast stretching Contrast stretching Hybrid median filter Morphological operations Local histogram equalization Geometric mean filter Gamma equalization Dark stretching Mean filter Adaptive median filtering

3.5.3.1 Erythrocyte segmentation

Various segmentation techniques have been proposed for the erythrocytes analysis using thin blood smear images, with better accuracy as shown in Table 6.4. From these, it has been observed that the techniques such as morphological approach, region based segmentation, thresholding, edge detection, segmentation based on partial differential equation, clustering based method have been used to address the cell segmentation problem [40,57,58,72e78,97]. Halim et al. [77] used a template matching using crosscorrelation based erythrocytes segmentation. Otsu’s thresholding with morphological filtering has been used in several studies for erythrocytes segmentation from the background [28,35,40,73,74,97]. However, it has a limitation where the variation in texture is very high and also unable to segment the overlapping erythrocytes into isolated cells. To overcome the issue of overlapping erythrocyte segmentation, the watershed segmentation with distance transform has been introduced [28,43,45,63,79e81]. The main drawback of

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Table 6.4: Erythrocyte segmentation methods for microscopic blood smear images. Literature

Segmentation methodology

Ruberto et al. [15e19], Mu ¨hlen et al. [72], Das et al. [40], Soni et al. [57], Ahirwar et al. [58], Panchbhai et al. [73], Sharif et al. [74], Memeu et al. [75], Walliander et al. [76] Halim et al. [77]

Morphological approach based on granulometries

Sio et al. [52], Kumarasamy et al. [78] Springl [28], Anggraini et al. [35], Das et al. [40], Savkare et al. [79], Kumar et al. [80], Ahirwar et al. [58], Savakare et al. [43], Panchbhai et al. [73], Sharif et al. [74], Mehrjou et al. [45], Malihi et al. [46], Bairagi et al. [81], Devi et al. [30e33] Springl [28], Savkare et al. [79], Maity et al. [63], Savkare et al. [43], Mehrjou et al. [45], Bairagi et al. [81] Diaz et al. [51] Zou et al. [53] Makkapati et al. [82] Vromen et al. [83] Wang et al. [84] Berge et al. [36], Damahe et al. [85], Mushabe et al. [49] Nasir et al. [60,61], Savkare et al. [44] Malihi et al. [46] Khan et al. [37], Sharif et al. [74], Das et al. [29,40,41] Muda et al. [86]

Kareem et al. [87,88], Charpe et al. [89] Berge et al. [36]

Maity et al. [63], Patankar et al. [59] Suryawanshi et al. [90] Vink et al. [91] Walliander et al. [76] Prasad et al. [92], Linder et al. [93] Puwar et al. [94], Widodo et al. [95] Kanafiah et al. [96] Devi et al. [30e33]

Template matching using crosscorrelation Rule based segmentation Otsu’s thresholding

Watershed transform with distance transform Color pixel classification with inclusion-tree Circle hough transform Color based method Contour tracing approach Shape reconstruction and multi-scale surface fitting Zack thresholding K-means clustering Canny edge detection Marker-controlled watershed with morphological approach Hybrid segmentation algorithms based on K-means and median-cut Annular ring ratio Concavity point detection based on Delaunay triangulation Threshold based segmentation Poisson distribution thresholding Pixel classification with connected component labeling Adaptive histogram thresholds Histogram thresholding with morphological approach Active contour method Radial-based cell formation (RCF) algorithm Marker-controlled watershed with h-minima approach

162 Chapter 6 the watershed segmentation algorithm is the over segmentation. To address the over segmentation issue, several techniques such as color pixel classification with inclusiontree, circle Hough transform, color based method have also been introduced [51,53,82]. An automatic erythrocyte segmentation technique based on contour tracing has been studied [83]. Here, the fusion technique of second order polynomial model and a simple Bayesian approach has been utilized for smooth boundaries. Further, the noise contours were reduced by using postprocessed ellipse fitting procedure. Wang et al. [84] utilized 3D shape feature extraction method for erythrocytes segmentation. Further, erythrocyte segmentation has been done based on different techniques such as thresholding, clustering, K-means, and canny edge detection [36,44,46,49,60,61,85]. Another approach based on Fusion technique [74] (YCbCr color conversion follow by masking, morphological operation and marker-controlled watershed segmentation) has been used to segment the cells. The advantage of the marker-controlled watershed algorithm is that it can mark the presence of the cells in case of overlapping, to segment into isolated cells. Mude et al. [86] introduced a hybrid segmentation algorithm based on K-means and Median-cut. The analysis of the individual erythrocyte is not possible with the hybrid segmentation method. A novel method to detect the erythrocyte has been proposed using the annular ring method in blood smear images. This process has been performed directly on the grayscale image without correcting the nonuniform illumination of images. This technique utilizes the information of cell size acquired using a granulometric process with intensity values. The total cell numbers are counted by allocating the cell position with an accuracy of 98% [89]. An algorithm has also been proposed to identify and count the total erythrocyte present in an image. To further increase segmentation performance, iterative analysis has been used. Further, Delaunay triangulation-based concavity point detection method has been used for clump splitting [36]. Further, different techniques such as threshold based segmentation, Poisson distribution thresholding, pixel classification with connected component labeling, adaptive histogram thresholds, histogram thresholding with morphological approach, active contour method, and radial-based cell formation (RCF) algorithm have also been proposed to improve the erythrocyte segmentation performance [59,63,76,90e96]. 3.5.3.2 Infected erythrocyte and parasite segmentation

For diagnosis of malaria infection, parasites or infected erythrocytes play an important role in parasitemia estimation. The different techniques used for infected erythrocyte and parasite segmentation have been shown in Table 6.5. From these, it has been observed that the issues of parasite or infected erythrocyte segmentation have been addressed using various methods such as morphological approach, histogram thresholding, supervised and unsupervised pixel classification. The infected erythrocyte segmentation can be done only using the thin blood smear images. However, the parasite segmentation can be done in thin

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Table 6.5: Malaria infected region segmentation methodology. Segmentation methodology Literature Ruberto et al. [15] Mu ¨hlen et al. [72] Halim et al. [77]

Infected erythrocyte Morphological approach

Variance-based approach, color co-occurrence matrix Non-parametric histogram Histogram thresholding Otsu’s thresholding using HSV color

Tek et al. [23,27] Toha et al. [98] Makkapati et al. [82] Mandal et al. [99] Damahe et al. [85] Zou et al. [53] Ross et al. [34], Ghosh et al. [100] Anggraini et al. [35], Somasekar et al. [39] Ghosh et al. [42,48]

Normalized cuts Zack thresholding Color channel difference Threshold based segmentation Threshold based segmentation Fuzzy divergence based segmentation Quaternion fourier transform Color based segmentation Morphological gradient method with K-median algorithm Morphological approach followed by thresholding Dark stretching technique Bayesian classifier RGB color pixel classification based on k-NN. Fuzzy rule base segmentation

Fang et al. [101] Koppar et al. [102] Yunda et al. [103]

Elter et al. [104] Hanif et al. [68] Cesario et al. [105] Mushabe et al. [49] Chayadevi et al. [106] Tsai et al. [69] Nasir et al. [60] Somasekar et al. [67] Khan et al. [107], Nanoti et al. [62] Maysanjaya et al. [47]

Parasite Morphological approach

Weighted Sobel operation Moving K-means clustering Fuzzy C-Means clustering K-means clustering Color combination with Otsu’s thresholding

as well as thick smear images. The morphological approach has been used for both the infected erythrocyte and parasite segmentation [15,97]. For the parasite segmentation, the techniques such as variance-based approach, color co-occurrence matrix, nonparametric histogram, histogram thresholding, Otsu’s thresholding using the HSV color model have been used [23,27,82,97,98]. Mandel et al. proposed a normalized cut segmentation method for erythrocyte segmentation. Zack thresholding has also been used for infected

164 Chapter 6 erythrocytes segmentation [99]. Moreover, the techniques such as thresholding based segmentation, fuzzy divergence based segmentation, quaternion Fourier transform and color-based segmentation were used for the parasite segmentation in the thin blood smear images [30,34,39,42,48,100e102]. In thick smear images, the parasite segmentation has been done using the techniques such as a morphological gradient method with K-median algorithm, the morphological approach followed by thresholding and dark stretching technique [68,101,104]. Supervised classifier such as Bayesian, k-NN were also used for parasite detection in thin blood smear images [49,105]. Chayadevi et al. proposed a fuzzy rule base parasite segmentation technique [106]. Tsai et al. [69] used the weighted Sobel operation to segment the infected erythrocyte for further detection of malaria species lifecycle. Unsupervised classifiers, i.e., fuzzy C-means clustering and K-means clustering were also used for parasite segmentation in thin as well as thick smear images [60,62,67,107]. Recently, Maysanjaya et al. [47] proposed a parasite segmentation technique based on color combination with Otsu’s thresholding. 3.5.4 Microscopic feature extraction For medical image analysis, feature extraction is the quantitative measures used to recognize the objects for analysis of various pathological structure and tissues present in the images. To enhance the performance of overall recognition rate and minimizing the computational complexity [108], appropriate and significant features are required. For classifying the various life-cycles of malaria, significant features have been explored. The features include morphological feature, and intensity and texture feature [109,110] for recognition of malaria’s life-cycles. Morphological features describe the overall size as well as shape of cell without considering the density. In P. ovale and P. vivax condition, the erythrocyte’s morphological feature got disturb, i.e., enlarged whereas it remain unaffected in P. falciparum infection [111,112]. Morphological features constitute of features such as Hu’s invariant moments, roundness ration, eccentricity, bending, perimeter, area, compactness, area granulometry, regional extrema and axis of the best fit ellipse (major and minor) [23,34,51]. The spatial distribution of the particular region which comprises of color histogram, entropy, local binary pattern (LBP), Laplacian texture, gray level co-occurrence matrix (GLCM), gradient texture, gray level run length matrix (GRLM), and color channel histogram such as saturation histogram, green channel histogram, etc., are used to describe the intensity and texture features [25,26,109,110,112]. For diagnosing the malaria parasite infection in erythrocyte, a feature set comprises of Hu’s invariant moments, relative shape measurements, scale invariance, color histogram, color auto-correlogram, area granuometry, and number of colors has been used [23,25,26]. Based on relative size along with eccentricity features, an automatic image analyzing technique has been proposed to classify the malaria-infected erythrocyte [34]. Moreover, the histogram feature has also been used [51]. Springl utilized the feature sets containing the intensity histogram, Hu’s invariant moment, flat texture, GRLM, GLCM, relative shape

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and size measurement, gradient features, and Laplacian features to classify the infected erythrocytes [28]. Features such as perimeter and form factor are also used [38]. A content-based image retrieval for malaria parasite detection has been proposed based on the feature set containing intensity histogram and Hu’s invariant moment [37]. The combination of the statistical and colors features (i.e., perimeter, compactness ratio, area, metric, grayscale histogram, and saturation histogram) has been used for malaria detection [43,79]. Further, the various features such as nucleo-cytoplasmic ratio, Euler number and Nuclear density have also been explored for malaria identification [78]. The standard deviation of the value channel of the HSV representation, ring part stained pixels and chromatin dot stained pixels percentage are also used [92]. A web accessible framework based on textural features GLCM, GRLM, LBP, and fractal dimension has also been developed for automated storage and malaria parasite classification [63]. Mobile support malaria diagnosis system using Hu’s invariant moment, relative shape measurement, color histogram, color auto-correlogram features has been proposed [105]. Ahirwar et al. [58] used gray level texture, geometric features and color attributes for automatic characterization and malaria parasite classification. Plasmodium vivax characterization has also been performed based on fractal dimension and GLCM [48]. A 96-dimensional feature set comprises of textural (Haralick texture, entropy, fractal dimension, LBP, histogram based features, GRLM, GLCM, shape features) and morphological features (Hu’s invariant moment) has been proposed for automatic characterization of malaria-infected erythrocytes [29,66]. Gradient, color histogram, area granulometry and flat texture has been used for malaria diagnosis [46]. Various color channel histogram, i.e., hue, saturation are also used for malaria-infected erythrocyte characterization [113e116]. Sriram et al. [54] used the morphological features (i.e., convexity, compactness, form factor, area) as well as histogram features to characterize the malaria infected erythrocytes. Different combination of textures, intensity and morphological features have been utilized for automatic malaria parasite recognition in blood smear images [106]. Memeu et al. [75] performed a rapid malaria diagnosis based on roundness, aspect ratio, extent, form factor, compactness and convexity of the cell as well as statistical moment. Moreover, phase of the image, kurtosis, skewness, standard deviation, and energy are also used [95]. A feature set of local binary pattern-rotation invariant local contrast, and scale invariant feature transform has also been utilized for malaria diagnosis [93]. Recently, the comparative analyses of various texture features, i.e., statistical features, GLCM, GRLM, fractal dimension, histogram features, LBP, Hu’s invariant moment, and entropy have been performed to classify the different malaria parasite species [62,81] (Table 6.6).

166 Chapter 6 Table 6.6: Microscopic features for malaria infected erythrocyte classification. Features Literature

Morphological

Ruberto et al. [15e18] Diaz et al. [51]

Granulometry, regional extrema

Tek et al. [23]

Hu’s moment, Relative shape measurements Roundness ratio, bending energy, chain code Size ratio (infected and noninfected erythrocyte), number of chromatin, number of parasites per erythrocyte, location Hu’s invariant moment, Relative shape measurement

Ross et al. [34] Semanet al. [111]

Springl et al. [28]

Tek et al. [25,26]

Das et al. [40] Edison et al. [38] Yunda et al. [103] Khan et al. [37] Savkare et al. [43,79] Kumarasamy et al. [78]

Prasad et al. [92] Maity et al. [63] Cesario et al. [105] Ahirwar et al. [58] Das et al. [41]

Area granuometry, relative shape measurements, Hu’s invariant moment, scale invariance Area, perimeter, circularity, orientation, compactness Perimeter, area, form factor

Hu’s moments Area, perimeter, compactness ratio, metric Nuclear density, nucleocytoplasmic ratio, Euler number Percentage, standard deviation

Hu’s invariant moments, Relative shape measurement Geometric (shape, size)

Texture and intensity Color histogram, saturation level histogram, gray scale histogram, Tamura texture histogram, Sobel histogram Color histogram Color channel intensity, Haralick texture, GRLM Texture of erythrocyte

Intensity histogram, gradient features, Laplacian features, flat texture, GLCM, and GRLM Color auto-correlogram, color histogram

GLCM

Haralick texture, Wavelet, color features (R, G, B) Intensity histogram Saturation histogram, gray scale histogram

Fractal dimension, GLCM, GRLM, and LBP Color auto-correlogram, color histogram Gray level texture, color histogram Entropy (Havarda and Charvat, Kapur, Renyi, Yeager measure, Shannon), GLCM, fractal dimension, LBP, histogram features, and GRLM

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Table 6.6: Microscopic features for malaria infected erythrocyte classification.dcont’d Features Literature Kareem et al. [88] Patankar et al. [59] Ghosh et al. [48] Vink et al. [91]

Morphological Relative size and geometry

Chavan et al. [113], Annaldas et al. [114], Nugroho et al. [115] Nithyaa et al. [116]

Malihi et al. [46] Sriram et al. [54]

Area granuometry, Hu’s invariant moment Convexity, compactness, form factor, area.

Chayadevi et al. [106] Memeu et al. [75]

Bairagi et al. [81] Nanoti et al. [62]

Devi et al. [31]

Intensity, and histogram Color texture Fractal dimension, and GLCM Histogram features, Haralick texture, color channel intensity, Histogram based features (mean, variance, kurtosis, skewness, energy, entropy) Hue histogram, saturation histogram, Intensity histogram Gradient, flat texture, color histogram, Histogram features Fractal dimension, color channel intensity

Form factor, roundness, aspect ratio, Solidity, extent, compactness, convexity, and statistical moment

Linder et al. [93]

Charpe et al. [89] Das et al. [29,66]

Texture and intensity

Shape, and size Shape features, and Hu’s invariant moment

LBP-rotation invariant local contrast, scale invariant feature transform Color, intensity, texture Entropy (Havrda and Charvat’s, Kapur’s, Renyi’s, Yeager’s measure, Shannon), Haralick texture, fractal dimension, LBP, histogram based features, GLCM, and GRLM Statistical features, and GLCM GLCM, GRLM, fractal dimension, histogram features, LBP, Hu’s invariant moment, and entropy Green channel histogram, saturation channel histogram, Chrominance channel histogram, R-G channel difference histogram

168 Chapter 6 Table 6.6: Microscopic features for malaria infected erythrocyte classification.dcont’d Features Literature Devi et al. [32]

Devi et al. [33]

Morphological

Texture and intensity Prediction error, Shannon entropy, Renyi’s entropy, Havrda and Charvat’s entropy, Kapur’s entropy,co-occurrence of local binary pattern (LBPGLCM), Green channel histogram, saturation channel histogram, Chrominance channel histogram, R-G channel difference histogram Prediction error, Shannon entropy, Renyi’s entropy, Havrda and Charvat’s entropy, Kapur’s entropy,co-occurrence of local binary pattern (LBPGLCM), green channel histogram, saturation channel histogram, Chrominance channel histogram, R-G channel difference histogram, and Gabor features

3.5.5 Feature selection For the scope of pattern recognition or machine learning, the domain of features have broadened up to hundreds used for different applications. The features which have no correlation to the classes might introduce bias in the predictor and the classification performance has reduced. The feature selection helps to choose the most significant feature subset from the large set of features. Various algorithms are established to address the issues of removing the irrelevant features which are a burden on classification tasks [117e119]. In malaria detection, feature selection techniques have been used to pick only the relevant features from the extracted features. Ross et al. [34] used the principal component analysis to reduce the feature vector. Elter et al. [104] established a two-stage system utilizing the univariate discriminative along with genetic algorithm. First, a univariate ranking is applied to retain the 60 features from 174 dimensional features, which have the extreme univariate discriminative power. Further, genetic algorithm was used for choosing the smallest feature subset automatically. Gosh et al. [48] introduced the feature selection methods such as t-test, box plot and kernel density estimation. The statistical test based techniques, i.e., ANOVA (F-statistics), box-whisker plot, and class

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conditional density plot were used to select only the relevant features from 96 dimensional features set [29]. The feature ranking has been done based F-statistics [62,65,66] and information gain [41,66] for selecting the most significant features for malaria diagnosis. Devi et al. analyzed the various combinations of the features for erythrocytes classification [31]. Further, the various feature selection techniques such as sequential forward and backward search (SFS, SBS), incremental feature selection along with different techniques such as scatter matrices, divergence, ANOVA (Analysis of variance), Bhattacharyya distance, and KruskaleWallis [32,33] has used to select reduce feature set. 3.5.6 Malaria infection identification Malaria diagnosis is a computer-assisted pattern classification problem using microscopic features of malaria-infected erythrocyte images. Various pattern classifiers used for erythrocyte analysis are shown in Table 6.7. It consists of Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Naive Bayes, Artificial Neural Network (ANN), Multilayer Perceptron (MLP), Euclidean distance classifier, multivariate regression, Ada-Boost, and hybrid classifier. 3.5.6.1 k-Nearest neighbor

It is defined as a nonparametric density estimation classifier with on closest training feature samples for object classification. The training procedure is simply based on storing the training samples together with the corresponding class labels. The object classification has been done based on the decision made by the majority votes of k nearest neighbors [24]. For binary classification problem, the k value is selected to be an odd number to avoid tie votes. Various distance functions such as Euclidean distance, Manhattan distance, and Chebyshev distance may be used. However, the Euclidean distance is the usually used distance function in k-NN classifier. Several studies used the k-NN classifier for the parasite and nonparasite pixel detection [50]. The advantages of k-NN are its simplicity, intuitive, and successfully applicable in medical image analysis. 3.5.6.2 Neural network

An ANN is an information processing system which behave as a great classifier for pattern recognition. It has some remarkable advantages to derive meaning from complicated data. It helps to recognize the complicated patterns to be identified by either human or computer techniques. Based on the provided data for training, the network has the capability to learn the task to be done [30,34,37,41,75]. Ross et al. [34] utilized a feed-forward back-propagation network in which the networks are designed by specifying architecture, and then the network is trained using a training rule on the training data to set the synaptic weights. It has the capability to test the data

170 Chapter 6 Table 6.7: Performance of the state-of art classification methodology for malaria detection. Literature

Classification methodology

Tek et al. [24]

k-NN classification methods

Ross et al. [34] Diaz et al. [50]

Feed-forward back-propagation neural network k-NN classification methods

Diaz et al. [51]

SVM

Khan et al. [37] Anggraini et al. [35]

Feed-forward back-propagation neural network Multilayer perceptron

Soni et al. [57]

Morphological approach

Das et al. [40] Savkare et al. [79]

Multivariate logistic regression model SVM with “rbf” kernel

Das et al. [41]

Multilayer perceptron (MLP)

Memeu et al. [75]

Vink et al. [91]

Feed-forward back-propagation neural network SVM Naı¨ve Bayes SVM Bayesian classifier Ada-Boost

Suryawanshi et al. [90] Maity et al. [63]

Euclidean distance classifier Naı¨ve Bayes’ tree

Kumarasamy et al. [78] Memeu et al. [75] Annaldas et al. [114] Linder et al. [93]

SVM Artificial neural network SVM SVM

Bairagi et al. [81] Devi et al. [31] Devi et al. [32]

SVM ANN Hybrid (SVM, k-NN and Naı¨ve Bayes with majority voting) Hybrid (SVM, k-NN and ANN with majority voting)

Das et al. [29] Ghosh et al. [48]

Devi et al. [33]

Performance statistics (%) Sensitivity: 74, specificity: 98, positive predictive value: 88, negative predictive value: 95 Sensitivity: 85 Positive predictive value: 81 F measure (erythrocyte): 99 F measure (parasite): 83 Erythrocyte: Sensitivity: 94, Specificity: 99.7 Infection stage: Sensitivity: 78.8, Specificity: 91.2 Sensitivity: 85.5 Positive predictive value: 81 Sensitivity: 92.59, specificity: 99.65 Positive predictive value: 67.50 Sensitivity: 98 Positive predictive value: 96 Accuracy-88.77 Sensitivity: 93.12 Specificity: 93.17 Accuracy: 96.73, positive predictive value: 98.64 Sensitivity:99.72, Specificity:84.39 Accuracy: 91 Accuracy: 84 Accuracy: 83.5 Accuracy: 95 Accuracy: 98 Sensitivity: 75 Specificity: 99.99 e Sensitivity: 99 Specificity: 99.80 Accuracy: 80 Accuracy: 79 Accuracy: 98.25 Sensitivity: 92.5, specificity: 100 Accuracy: 97.7 Accuracy: 96.32 Accuracy: 98.50 Accuracy: 96.54  0.73

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that were not presented to it during training. Some studies have also used feed-forward back-propagation networks for parasitemia estimation and parasite detection, etc. [37,75]. Anggraini et al. [35] proposed an erythrocytes classification model based on multilayer perceptron. It is a feed-forward neural network having multiple layers of nodes in a directed graph, which utilized supervised learning technique to train the network. Moreover, MLP has also been used for the classification of erythrocytes into P. falciparum (gametocyte, ring), P. vivax (gametocyte, schizont, ring), and noninfected [41]. 3.5.6.3 Support vector machine

SVM is a discriminative classifier characterize by a separating hyperplane. The class separation hyperplane is optimized by maximizing the distance between the hyperplane separating the classes and the object pattern [29,48,78,81,120]. Diaz et al. [51] separate the erythrocyte pixels from the background pixels using the normalized RGB color space.Saturation level histogram, gray level histogram, Tamura texture histogram, Sobel histogram, and color histogram were used and finally erythrocyte was recognized. Multiclass SVM was used to classify the erythrocytes into various life-cycle stages which achieved satisfactory performance of parasite detection with sensitivity 78.8% and specificity 91.2% [93,114]. SVM with radial basis function provides sensitivity of 93.12% and specificity 93.17% for characterization of malaria infected erythrocytes [79]. 3.5.6.4 Naı¨ve Bayes

Bayes’ theorem is a probabilistic classifier with independence assumptions between the features [29,48]. The class with largest posterior probability is chosen to assure the minimum classification error. In Naı¨ve Bayes classifier, the feature distributions play a vital role to increase the classification performance. Naı¨ve Bayes classifier is working well in many areas especially in the medical applications [63]. It provides an overall accuracy of 83.5% [25] and 98% [44] in malaria infected erythrocytes classification for life-cycle stages analysis. 3.5.6.5 Multivariate regression

Das et al. [40] used the multivariate logistic regression model for detecting the infected erythrocytes with Plasmodium vivax. Least square estimation of linear regression does not provide robustness. So, multivariate logistic regression has been fitted to accomplish robustness to calculate the patient disease probability depend on erythrocyte features (geometrical along with textural features). The multivariate logistic regression-based malaria infected erythrocyte classification provides an accuracy of 88.77%.

172 Chapter 6 3.5.6.6 Ada-Boost

Ada-Boost was first formulated by Yoav Freund and Robert Schapire. It is an ensemble method that creates a strong classifier from a set of weak classifiers [121]. It is based on its straightforward use, real-time target detection performance, feature selection, and its quick training time. Ada-Boost is mainly used to boost the decision tree’s performance on binary classification problems. A weak classifier is prepared with the training data using the weighted samples. Using training data, a function has been created that designs a pair of feature values as inputs to outputs. This method has been used for malaria infected erythrocyte analysis [87]. 3.5.6.7 Euclidean distance classifier

Linear Euclidean distance classifies the feature data points linearly. It partitions the feature space using a decision boundary into two separate decision regions. Suryawanshi et al. proposed an improved technique for malaria parasite detection using Euclidean distance classifier [90]. 3.5.6.8 Hybrid classifier

Hybrid classifier is developed by the combining the decision of the multiple classifiers. It helps to improve the classification rate and also suppress the misclassification rate of the other individual classifiers. Devi et al. proposed a hybrid model by the fusion of SVM, Naı¨ve Bayes and k-NN for infected erythrocyte classification [122,123]. Further, various hybrid models have been studied to classify the erythrocytes into seven different lifecycles [124]. Hybrid classifier (i.e., fusion of individual classifiers) may be developed using different combining techniques [33]. 3.5.7 Computer-aided malaria diagnosis In the computer-assisted malaria diagnosis, the different approaches such as erythrocytes classification into different life-cycle stages and parasitemia estimation are used [125e129]. The various computer-assisted malaria diagnosis systems are shown in Table 6.8. The parasitemia estimation methodologies are also shown in Table 2.7. A twostage analysis method to classify the infected erythrocyte into P. falciparum, P. vivax, P. ovale, and P. malariae species has been proposed [34,37,57,130]. Parasite and nonparasite classification systems have also been developed [24]. A classification system, which adapts to the pixel separation problem into three different classes, i.e., parasite, erythrocytes, and background has been discussed [50]. Tek et al. [25,27] proposed an automatic detection with identification of malaria parasites. This system detects the parasite and nonparasite pixels. Further, the parasites have been classified into four different species and species into different life-cycles.

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Table 6.8: Various diagnosis methods using thin blood smear. Diagnosis system Literature

Classes

Species and life-cycles

Ross et al. [34], Soni et al. [57], Khan et al. [37], Kurer et al. [130] Tek et al. [24] Diaz et al. [50]

Four

P. falciparum, P. vivax, P. ovale, and P. malariae

Two Three

Tek et al. [25,27]

Twenty

Anggraini et al. [35]

Four

Das et al. [40], Prasad et al. [92], Ahirwar et al. [58], Mehrjou et al. [45], Memeu et al. [75], Nasir et al. [61] Nasir et al. [60], Bairagi et al. [81] Ghosh et al. [48]

Two

Parasite and nonparasite Parasite, erythrocytes, and background Parasite, nonparasites, species (P. vivax, P. falciparum, P. ovale, P.malariae) and life-cycle (ring, trophozoite, schizont and gametocyte) for each species Noninfected erythrocyte, erythrocyte with artifacts, ring, and gametocyte Infected and noninfected erythrocyte

Three Two

Linder et al. [93]

Two

Das et al. [29,41,66], Maity et al. [63]

Six

Nugroho et al. [115]

Three

Nanoti et al. [62]

Twelve

Devi et al. [31,32]

Two

Devi et al. [33]

Seven

ring, trophozoite, and gametocyte P. vivax infected and noninfected erythrocyte P. falciparum infected and noninfected erythrocyte P. falciparum (gametocyte, ring), P. vivax (gametocyte, schizont, ring), and noninfected P. falciparum (trophozoite, schizont, and gametocyte) P. vivax (trophozite, schizont and gametocyte), P. falciparum (trophozite, schizont and gametocyte), P. ovale (trophozite, schizont and gametocyte), and P. malariae (trophozite, schizont and gametocyte) Infected and noninfected erythrocyte Noninfected, P. falciparum ring, P. falciparum schizont, P. vivax ring, P. vivax ameboid ring, P. vivax schizont and P. vivax gametocyte

174 Chapter 6 Computerized diagnosis method for erythrocytes classification into noninfected erythrocyte with artifacts, ring, and gametocyte has been proposed [35]. The single-stage analysis system has also been developed to classify the erythrocytes (2 classes) [40,45,58,61,75,92]. Ring, trophozoite, and gametocyte of Plasmodium species have been classified to detect the severity of the malaria [60,81]. Ghosh et al. [48] proposed a system for quantitative characterization of Plasmodium vivax in the infected erythrocyte. Computer vision screening and visualization of Plasmodium falciparum in the infected erythrocyte has also been proposed. An automatic characterization and classification system for malaria-infected erythrocytes (i.e., P. falciparum (gametocyte, ring), P. vivax (gametocyte, schizont, ring), and noninfected) has also been developed [29,41,63,66]. Nugroho et al. [115] proposed an infected erythrocyte classification system into three classes (P. falciparum trophozoite, P. falciparum schizont, and P. falciparum gametocyte). Nanoti et al. [62] classify the Plasmodium species into 12 different life-cycles, i.e., P. vivax (trophozite, schizont, and gametocyte), P. falciparum (trophozite, schizont, and gametocyte), P. ovale (trophozite, schizont and gametocyte), and P. malariae (trophozite, schizont, and gametocyte). From Table 6.8, it has been observed that the parasitemia estimation has been done using thin blood smear and thick blood smear images, stained with Leishman and Giemsa staining methods. Bejon et al. [131] used Poisson distribution methods for parasitemia estimation in Giemsa stained thick blood smear images. Infected erythrocyte detection was done based on two different methods such as variance-based approach and color co-occurrence matrix [77]. Morphological approach and Zack thresholding has also been used [52,132]. The parasitemia estimation method has been developed using edge-based parameters which characterizes the malaria-infected erythrocytes. Giemsa stained thin blood smears [52]. Le et al. [132] established a comparison-based analysis, that can differentiate the solid components in blood smears. This semiautomatic method employ statistical measures and a reliable detection scheme is obtained by using cross-referencing validations. Further, the nucleated components are detected using adaptable spectral information. Cells are differentiated from parasites as well as background by comparing the input image with empty field of view image. Another unsupervised technique for parasitemia estimation has been done by extracting the erythrocytes (normal and parasite infected). A precision and recall rates of 80%e88% and 92%e98% has been observed for normal erythrocyte detection, respectively. Further, the precision and recall rates of 92% and 95%, respectively, has also been observed with a training-based method. Zou et al. [53] proposed a system using circular Hough transform and color channel difference to extract the erythrocytes and parasites on the thin blood smear, respectively, to estimate the parasitemia.

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Edison et al. [38] performed the parasitemia estimation using the Sobel edge detection, gray intensity thresholding. Purwar et al. [94] developed a probabilistic k-means clustering approach for parasite enumeration. Savkare et al. [79] proposed an automatic malaria parasite detection technique in smear images by extracting erythrocytes and classify as normal or parasite infected. Parasite identification methodology using SVM with rbf kernel provides 93.12% sensitivity and 93.17% specificity for automatic parasitemia. Kumarasamy et al. [78] proposed SVM-based parasitemia detection methodology where they have achieved 80% accuracy. Histogram thresholding based on Zack’s thresholding technique has been used for parasitemia estimation [49]. Parasite enumeration methodology using artificial neural network provides 79% accuracy [75]. A serious parasitic infection detection has been done based on digital processing techniques for efficient diagnosis [100]. An accuracy of 96.46% has been reported for parasite enumeration based on the morphological approach [55]. Moreover, Devi et al. proposed a various systems, first, erythrocyte classification into infected and noninfected, and the system has been extended to distinguish the malaria life-cycles i.e., ring (P. falciparum,P. vivax), schizont (P. falciparum,P. vivax), ameboid ring (P. vivax), gametocyte (P. vivax), and noninfected.

4. Discussion In this survey, a systematic reviewed has been performed for malaria prediction and screening, by analyzing the importance, advantages and disadvantages of the various techniques. From the study, it observes that the malaria prediction based on the big data analysis done by the environmental data and clinical data provides better results. For malaria diagnosis, comparative analysis of the various techniques has been performed and shows that the computerized diagnosis requirements are quite similar to manual examination, with less time consuming. The computerized diagnosis system can be of different approaches, i.e., erythrocyte analysis, parasite and infected erythrocyte segmentation. The erythrocyte analysis system consists of preprocessing of the blood smear, erythrocyte segmentation, feature extraction, feature selection, and classification. In the preprocessing stage, the techniques such as median filtering, geometric mean filter, and adaptive median filter have been used. Later, erythrocytes have been segmented from the background using various segmentation techniques. The segmentation techniques include Otsu’s thresholding, watershed segmentation, and marker-controlled watershed segmentation. From the segmented erythrocytes, the intensity, texture, and morphological features have been extracted to describe the erythrocyte. Different feature selection techniques have also been explored to select only the efficient features. Further, the erythrocytes have been classified into various life-cycles of the parasite. The various classifiers such as SVM, Naı¨ve Bayes, k-NN, ANN, and hybrid classifier have been used to classify the erythrocytes. In parasite segmentation systems, supervised as well as

176 Chapter 6 (A)

(B)

Segmentation

Eryrthrocyte

Infected- erythrocyte

Feature Extraction

Morphological features Texture and Intensity feature

parasite

Morphological, Texture and Intensity features

18%

29%

41% 64% 41%

7%

(C)

Multivariate Classifiers Logistic regression Hybrid 8% 4% Morphological k-NN approach 7% 4%

Euclidean distance 4% Adaboost 4%

ANN 27% Naïve Bayes 11% SVM 31%

Figure 6.4 Graphical analysis of the importance of (A) segmentation, (B) feature extraction, and (C) classifiers.

unsupervised classification techniques, etc., have been used. The erythrocyte analysis system can be possible only with the thin smear images, whereas in parasite segmentation, either thin or thick smear can be used for the analysis. In this paper, the focus of the researchers toward the importance of erythrocyte segmentation, infected erythrocyte segmentation and parasite segmentation has also been analyzed as shown in Fig. 6.4A. From these, it has been observed that the 64% of the research work focus on erythrocyte segmentation for the analysis, as compared to parasite and infected erythrocyte segmentation. Further, in feature extraction, several researchers focused on the feature combination of morphological, texture and intensity features for the erythrocyte classification. Here, 41% of the research work are occupied by the texture and intensity features and combination of morphological, texture and intensity features as shown in Fig. 6.4B. Moreover, the importance of the different classifiers has also been analyzed as shown in Fig. 6.4C

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5. Conclusion In this study, the various analysis has been performed to identify the importance of malaria prediction as well as malaria diagnosis. With the help of the malaria prediction model, the chances of the occurrence of the malaria in that particular area can be detected and prevention can be done. Big data analysis using various data such as clinical data (patient past record), and environmental data helps us to control the malaria on time. Accurate predictive model can help to calculate the malaria impact and prevention can be done by proper allocation of medical resources. Extra effort is needed to explore the better predictive model for real-time application. Further, for malaria diagnosis, the computerassisted malaria diagnosis system has gained huge attention due to its reliability in comparison to other methods. From various literature surveys, it has been noticed that existing malaria diagnosis techniques analyzed the erythrocytes in two different approaches, i.e., erythrocyte analysis and parasite segmentation. The erythrocyte analysis can be addressed in two different approaches, i.e., single stage analysis and two-stage analysis. From the analysis of the different models for erythrocyte classification, the model performance varies with the variation in features, feature selection techniques, classifiers, and also variation in the database. The first problem in malaria diagnosis using thin blood smear is to segment the erythrocytes from the complicated background. There are various difficulties during this phase such as illumination correction, noise reduction, clump erythrocyte identification and clump erythrocyte segmentation, etc. The second problem is infected and noninfected erythrocyte classification which is an important phase for malaria diagnosis. This phase is influenced by the features and classifiers used for classification. Another problem is life-cycles classification for disease severity detection. For life-cycles classification, the challenging issues include disease specific features extraction, selection of most prominent feature subset from huge feature set and better classifier model to increase system performance. To address all these issues, different research work has been carried out. In future, the significance of both the predictive model and diagnosis model need to be analyzed.

Acknowledgments For database collection, we would like to express our gratitude to Dr. S. A. Sheikh, Silchar Medical College and Hospital, Assam and Dr. A. Talukdar, Head of the Department, Pathology, Cachar Cancer Hospital and Research Center, Assam.

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CHAPTER 7

Semantic interoperability in IoT and big data for health care: a collaborative approach Sivadi Balakrishna, M. Thirumaran Department of CSE, Pondicherry Engineering College, Pondicherry University, Puducherry, India

Chapter Outline 1. Introduction 186 2. State of the art 190 2.1 Internet of Things (IoT) 190 2.2 Cloud computing 193 2.3 U-health care system 194 2.3.1 Body Area Network (BAN) 197 2.3.2 Intelligent Medical Server (IMS) 197 2.3.3 Hospital system 197

3. Semantic interoperability 3.1 3.2 3.3 3.4

199

Ontologies and Standards 200 Mapping Technologies for Data Models 200 Data integration and exchange systems 200 Semantic annotations 200

4. Semantic interoperability in IoT health care 202 4.1 Adding semantic annotations to the IoT health care data 202 4.2 Experiments and results 204

5. SI in big data health care

205

5.1 Adding semantic annotations to the big data health care data 5.2 Experiments and results 208

6. Conclusion and future work References 218

208

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Handbook of Data Science Approaches for Biomedical Engineering. https://doi.org/10.1016/B978-0-12-818318-2.00007-6 Copyright © 2020 Elsevier Inc. All rights reserved.

185

186 Chapter 7

1. Introduction The Internet of things (IoT) is used to connect the things to the Internet and is a combination of IoT devices-sensors, actuators, and Radio Frequency Identification (RFID) tags and smoothly distributed smart IoT objects having the sensing abilities and actuating capabilities, embedding with IoT technology. IoT mainly addresses scalability, accessibility, visibility and controllability of the sensing smart objects and things. In future, the physical objects and digital objects have to be embedded and intercommunicated to obtain more domain specific applications [1]. IoT is concentrates on transforming the real-time objects into sensible smart objects with communicative and controllable environmental physical objects. RFID is the technology used to capturing of objects, people, and living and nonliving things. Electronic Product Codes (EPC) are embedded RFID tags to be used for tacking IoT smart things. Cloud and big data technologies are useful for storage and performing analysis on IoT data. The IoT has the midrange list of applications to be supported and suitable for smart city environments. Environmental monitoring [2], smart homes [3], health care applications [4], production and inventory management [5], supply chain management of food [6], smart cities [7], fire station systems [8], aerial vehicle data [9], VANETS [10], semantic real-time traffic management [11], social networks [12], and industry 4.0. From the year 2010 onwards, researchers have been analyzing and implementing IoTbased smart city applications by applying various kind of frameworks and approaches. To make the service as universally maintainable, web accessibility and open IoT, by combining web and IoT, emerges as the Web of things (WoT). To succeed with the adaptation of IoT to WoT, REST, the most appropriate architectural style, is used for IoT platforms [13]. The first to attain semantic interoperability (SI), merging IoT with REST principles for performing resource manipulation and uniform operation, which is the one approach for getting the IoT-based heterogeneous resources and gains the service accessible. Nevertheless, semantically annotated data is losing in IoT platforms and resource consignments are vertically deployable along with independent silos owing to the heterogeneity of data models and the low-level resource descriptions. To achieve SI, the key task is to combine the IoT via semantics. Undoubtedly, the term smart home will improve the human’s living day to day life become wider and smarter. The key idea for making things wider and smarter is to semantically interconnect the resources available in smart home domain. Our goal is reached by providing humans living life more reliable and maintainable. In the IoT applications, SI is the new and disruptive buzzword for exchange the resources information in a consistent manner. Billions of heterogeneous resources are connected to the Internet, not only from sensors and actuators but also from various IoT deployment models, a huge variety of data, high volume of data, and low-level descriptive resources. SI problem is carried out in these heterogeneous IoT resources. To

Semantic interoperability in IoT and big data for health care: a collaborative approach 187 accomplish SI in the Internet of things (IoT) is a vital challenge. In retort to this, toward an optimized SI framework has been proposed for generating the resources automatically to the corresponding semantic graphs through determining IoT-based smart home resources from RESTful principles, glossing descriptions of resources and to do operational behavioral of implicit links among IoT resources. In this chapter, the smart home resources have been taken and implemented through Restlet framework tool. The generated RDF graph is semantically interoperable and intercommunicated between the IoT-based smart home resources. Finally, the results show that the proposed framework is optimized toward the SI in IoT domains for smart home applications. IoT is an emerging technology between Internet and Communication Technologies. In simple words we can say that “IoT” is nothing but connecting living and nonliving things to the Internet. Traditionally “objects” can be treated as everything in the object-oriented programming languages. Similarly “smart objects” was treated as everything in the IoT platform and these smart objects allows communicating with each other through the Internet, physically or virtually. IoT helps for connecting people as well as things at anytime, anywhere, and anything using network path and service [14]. In the early 1990s, even though Internet connectivity helps in enterprise and consumer markets, it was still limited because of low performance of the network interconnects. In the 2000s, Internet connectivity began for enterprise, industrial, medical, business, and consumer products to provide them access to information. There was also still primarily resources on the Internet that required more attention to access those things or resources interacting and monitoring by applications and interfaces. According to Cisco, estimates that more than 50 billion connected devices to the Internet by 2020 will represent an almost five-fold increase from 10 billion in 2010. Even though IoT has supports for a wide range of worldwide acceptable standards and applications, it is still in starting stage and lot of scope for researchers to do research in high volume range of issues like protocols, heterogeneous devices, scalability, standards, Common Service Description Language (CSDL), discovery and reasoning, integration of IoT services and many more divergent issues. Achieving interoperability in IoT crossdomain applications is a big challenge. Interoperability is used to exchange the information from one place to another in an integrated manner. Interoperability is mainly divided into three categories, namely, syntactic interoperability, SI and organizational interoperability. To expose interoperability, in 1997 the Architectural Working Group (AWG) C4ISR has developed Levels of Information Systems Interoperability (LISI) application integration model [15]. The intention of LISI model is used to provide capability maturity model for DoD (US Department of Defense) and list out the interoperability issues and to managing with pragmatic approaches for achieve interoperability in enterprise level [16]. The IoT domain is closely moving from existing IP and Ethernet infrastructure approaches to interoperable migrated standards [17]. As per

188 Chapter 7 Cisco, there will be 50 billion connected devices by 2020 and these devices bring a big problem for maintaining the interoperability in IoT [18]. The IoT worldwide forums are hardly working to produce a common architecture that should ensure interoperability problem in IoT smart resources. The Open internet Consortium (OIC) is paying attention on interoperability problem in IoT to expose the available specifications, integration of billions of smart resources and address scalability disputes. Solving interoperability in low cost is an acceptable approach in smart city applications. Linksys and Soekris Engineering are the companies that have been developed and providing low-cost interoperability in IoT smart city applications. The Grid-Wise Architectural Council (GWAC) is also used to solve interoperability in IoT smart objects. The GWAC is implemented in a suitable framework to solve interoperability problems [19]. One way of solving the interoperability is to work with the different vendors using combined environment in the future. The result may include to solve the complexity of high-level infrastructure, low-cost business buildings, and to support heterogeneous resources. Fig. 7.1 depicts the smart application domains causes for lack of SI. Integration of heterogeneous merchants can also result in solving interoperability problems. The merchants have to collaborate and build-up transparent approach to implement the interoperability in every platform. Collaboration is required among the modeling enterprises for developing business architectures and software principles working on practical information technology environments [20]. Interoperability approaches are not

Figure 7.1 Semantic Interoperability (SI) in Smart City applications.

Semantic interoperability in IoT and big data for health care: a collaborative approach 189 platform specific. So, to achieve interoperability in all platforms, the communicated smart resources are interoperable in a semantic way [21]. Semantic will provide what data is exchanging and also clarifies the structure of exchanged data. To remove the conflicts in semantic transferring data, the IoT devices has includes the semantic details, integration of data, exchange of data with meanings. The description of the smart resources give more meanings and fully interoperable than the original means of the smart resources. A solid infrastructure is required for interoperability to apt any type of devices and merchants. Here the customer may feel free and use any IoT device from any manufacture at any time and vice versa. Interoperability is facing heterogeneous problems globally due to all IoT devices generated data is heterogeneous in nature. The various types of IoT devices from unknown merchants face various kinds of installation and compatibility issues. The IoT devices from multiple merchants have various functions for both semantic and syntactic interoperability in IoT platforms. So, there is a big problem occur to solve conflicts occurred in semantic and syntactic approaches. It is hard to add new device data in IoT networks without resolving ambiguity in semantics. In health care platforms, if the health care data is in “interoperable environment,” then high quality is provided to the patients. Here, high quality means communicating and exchanging information between the patients and physicians in an efficient and meaningful way. In hospitals, the patients and physicians have communicated remotely from any place and from anywhere. Physicians can get the patient information fast and recommend them for treatment so that fast access is placed to gather patient health care data stored in the cloud platforms. The main objective of this chapter is listed as follows: 1. To describe the overview of the recent technologies like IoT, Cloud Computing and U-health care systems. 2. To add the semantic annotations to achieve SI in IoT health care sectors using use-case approaches. 3. To add the semantic annotations to achieve SI in big data health care sectors using use case approach. 4. To extract and visualize the health care data using Resource Description Framework (RDF) framework and SPARQL queries. The remainder of this chapter is described as follows: In Section 2, the state of the art schemes includes some overview of IoT, cloud computing, and ubiquitous health care systems. In Section 3, the authors discuss the SI problem in IoT domains along with ontological standards. The SI in IoT health care and big data health care systems described in Sections 4 and 5, respectively. Finally. Section 6 concludes this chapter along with future scope.

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2. State of the art This section mainly describes IoT, ubiquitous health care (U-health care), and cloud computing.

2.1 Internet of Things (IoT) The main motivation applied behind IoT in U-health care is to improve the access and interconnection of devices used in U-health care. RESTful CoAP protocol take place an important role to deliver U-health care to people in remote locations and monitoring systems to provide a continuous stream of accurate data for better health care decisions [22]. Even though IoT Technology is better for collecting, analyzing, and transmitting data, still it has to improve the IoT-driven health care applications and systems emerge. In general, IoT devices gather and share information; using gateways the collected information was stored in the cloud environment through Internet. And finally making possible to collect and analyze new data streams faster and more accurately. The health care systems in IoT domain are categorized as services and applications. The applications again subdivided into two categories, namely, single-condition and doublecondition applications. Those all are listed out in following manner. So most of these services and applications need to communicate and exchange from one medium of service to another medium of service by integrating the things. At that place, interoperability problem has been occurred with a semantic annotation to the medium of services or applications. Finally, semantically interoperability role is placed at high-level. Table 7.1 shows the IoT in healthcare services and applications in a wide range.

Table 7.1: IoT in health care services and applications. IoT in health care Services Ambient assisted living Wearable devices access Embedded gateway configuration Internet of m-health Adverse drug reactions Children health information Embedded context prediction Semantic medical access Indirect emergency health care Community health care

Applications Single-condition Glucose level sensing ECG monitoring Oxygen saturation monitoring Blood pressure monitoring Body temperature monitoring Glucose level sensing

Clustered-condition Rehabilitation system Smartphone health care solutions Medication management Imminent healthcare Wheelchair management Rehabilitation system

Semantic interoperability in IoT and big data for health care: a collaborative approach 191 In Table 7.1, the IoT in health care systems show the various types of services and applications in ubiquitous health care application. For example, IoT collects the information or data from a range of industries; cars sense that data or wear and know the exact location of car dynamically whether it was in parking or driving or something else. Similarly, the trains dynamically calculate arrival timings of trains for waiting passengers. Communication can be done by any of the Intranet, Extranet, and Internet that are supported by the technologies such as Cloud Computing, SOA and RESTful CoAP protocols [23]. In Table 7.2 shows the various health conditions suffered from patients and the corresponding IoT roles is mentioned. In Table 7.3 shows the eHealth strategies published in various years along with their countries. The eHealth and U-health care systems are the more advanced mechanisms in the health care domain. Table 7.3 shows the various countries follows the eHealth strategies along with their established year. The revolution in this health sector is quietly brewing. IoT technology can be useful for connecting billions of devices and applications using sensors, microcontrollers, and actuators. These devices may helpful for better health monitoring and also provides more features like timely and convenient lowering costs. IoT in health care systems show the various types of services and applications in ubiquitous health care. For example, IoT collects the information or data from a range of industries; cars sense that data or wear and know the exact location of car dynamically whether it was in parking or driving or something else. Similarly, the trains dynamically calculate arrival timings of trains for waiting passengers. Communication can be done by any of the Intranet, extranet, and cloud computing, SOA, and RESTful CoAP protocols. As shown in Fig. 7.2, the three categories of interface standardization to establish a cooperative ecosystem have been presented, including hardware and software interfaces, health data formats, and security schemes. This can eventually ensure associated interoperability. With standardization policy, both application and platform providers are connected with health care suppliers, and they take the help of content providers to address the patients and telecom operators. Without standardization everyone is individually connected to their opposite partner. The public authority is connected to the health care service providers. The health care suppliers are connected to the service repository. Meanwhile, the application designer is merged with health care service providers and a service repository. The platform provider is connected with service repository and content provider is also have the same connection. The service repository is attached with the telecom operators. The entire flow of process is shown in Fig. 7.2. Fig. 7.3 depicts the various auxiliary healthcare apps for smartphones connected by IoT devices.

192 Chapter 7 Table 7.2: Health care conditions and IoT roles. Infirmity/condition

Sensors used; operations; IoT roles/connections

Diabetes

A non-invasive opto-physiological sensor; the sensor’s output is connected to the TelosB mote that converts an analog signal to a digital one; IPV6 and 6LoWPAN protocol architectures enabling wireless sensor devices for all IP-based wireless nodes. A smartphone camera; image decompression and segmentation; the app runs on the software platform in the smartphone’s system-onchip (SoC) to drive the IoT. Capacitive electrodes fabricated on a printed circuit board; digitized right on top of the electrode and transmitted in a digital chain connected to a wireless transmitter; BLE and Wi-Fi connect smart devices through an appropriate gateway. A wearable BP sensor; oscillometric and automatic inflation and measurement; Wireless Body Area Network (WBAN) connects smart devices through an appropriate gateway. A wearable body temperature sensor; skin-based temperature measurement; WBAN connects smart devices through an appropriate gateway. A wide range of wearable and smart home sensors; cooperation, coordination, event detection, tracking, reporting, and feedback to the system itself; Interactive heterogeneous wireless networks enable sensor devices to have various access points. Delamination materials and a suit of wireless biomedical sensors (touch, humidity, and CO2); the diagnosis and prognosis of vitals recorded by wearable sensors; the global positioning system (GPS), database access, web access, RFIDs, wireless links, and multimedia transmission. WBAN sensors (e.g., accelerometers, and ECG, and pressure); nodes process signals, realize abnormality, communicate with sink nodes wirelessly, and perceive surroundings; smart devices and data center layers with heterogeneous connectivity. A pulse oximeter wrist by Nonin; intelligent pulse-by-pulse filtering; ubiquitous integrated clinical environments. Smartphone cameras; visual inspection and/or pattern matching with a standard library of images; the cloud-aided app runs on the software platform in the smartphone’s SoC to drive the IoT. A built-in microphone audio system in the smartphone; calculates the air flow rate and produces flow-time, volume-time, and flow-volume graphs; the app runs on the software platform in the smartphone’s SoC to drive the IoT. A built-in microphone audio system in the smartphone; an analysis of recorded spectrograms and the classification of rainforest machine learning; the app runs on the software platform in the smartphone’s SoC to drive the IoT. A built-in microphone audio system in the smartphone; speech recognition and vector machine classification; the app runs on the software platform in the smartphone’s SoC to drive the IoT.

Wound analysis for advanced diabetes patients Heart rate monitoring

BP monitoring

Body temperature monitoring

Rehabilitation system

Medication management

Wheelchair management

Oxygen saturation monitoring Eye disorder, skin infection

Asthma, chronic obstructive pulmonary disease, cystic fibrosis Cough detection

Allergic rhinitis and noserelated symptoms

Semantic interoperability in IoT and big data for health care: a collaborative approach 193 Table 7.2: Health care conditions and IoT roles.dcont’d Infirmity/condition

Sensors used; operations; IoT roles/connections

Melanoma detection

A smartphone camera; the matching of suspicious image patterns with a library of images of cancerous skin; the app runs on the software platform in the smartphone’s SoC to drive the IoT. Surgical robot systems and augmented reality sensors; robot arms, a master controller, and a feedback sensory system giving feedback to the user to ensure telepresence; real-time data connectivity and information management systems.

Remote surgery

Table 7.3: National eHealth strategies in various countries. Country/region

eHealth strategy (year published)

Australia Australia Denmark

National eHealth strategy (2008) State eHealth strategy-Queensland (2006) National IT strategy 2003e07 for the Danish health care service (2003) European countries on their journey toward national eHealth infrastructures, evidence on progress and recommendations for cooperation actions (2011) eHealth priorities and strategies in European countries (2007)

The European Commission, DG for Information Society and Media, ICT for Health The European commission, DG for Information Society and Media, ICT for Health The European Commission Finland Kenya Mauritius Saudi Arabia Scotland Sweden Switzerland The U.S.

Repository of eHealth strategies and priorities for EU member states (N/A) eHealth roadmap e Finland (2007) National eHealth strategy (2011) National eHealth strategy: He@lth2015, seamless continuity of care (2010) National eHealth strategy (2011) National eHealth strategy (2011) National strategy for eHealth (2006) Swiss eHealth strategy (2007) Federal health IT strategic plan (2011)

2.2 Cloud computing The basic concept of cloud computing (CC) is providing applications and services from data centers through Internet to all over the world [24]. In this paper, the term cloud used for store health care data and perform computation based on user specifications and requirements. CC mainly have three types of services, these are listed as 1. Infrastructure as a Service (IaaS); 2. Software as a Service (SaaS); 3. Platform as a Service (PaaS).

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Figure 7.2 Platform interfaces (A) without standardization; (B) with standardization.

There are so many benefits from CC. The most important benefit is reduction of cost, because “you pay as you go.” Another one is portability of the application, i.e., the users can work from anytime, anywhere like home, work and client location, etc. CC offers excellent benefits for the users in health care sector; It provides the facilities for doctors, clients, hospitals, and health clinics to quick access for computing and large storage facilities which was not provided in the traditional settings. Moreover, health care data needs to be sending various geographical places so there is a burden for health care service provider and the patient causing significant delay in treatment and also lost the valuable time. Cloud caters solve all these problems by providing the health care systems to an incredible opportunity to improve services to their customers. The patients share the information more easily than ever before and also improve the operational efficiency on time. Ubiquitous server in the cloud may helpful to the cloud to give the faster data retrieval and flexibility. The hospital can provide access of the patient information ubiquitously. The RESTful CoAP protocols uses the cloud U-health care server provides to access the patient data to any device regardless of the location.

2.3 U-health care system Especially in medical treatment, U-health care is an emerging technology to increase efficiency, accuracy, flexibility, and availability. It provides convenient health care service

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Figure 7.3 Auxiliary health care Apps for smartphones.

between providers and patients, and it is easy to diagnose patient health conditions. Patients or people can monitor their health without visiting the hospital or clinic. Smart phones, laptops, and personal digital assistants (PDAs) have made Ubiquitous health care computing possible because those all are new to Market. These are available anytime and anywhere. Pervasive computing is nothing but interaction between people and electronic Computational devices. And these can be used in hospitals, emergency, critical situations, education and industry and also battlefield. Federica Paganell et al. [25] discuss that IoT was used for clinical care where hospitalized patients and their physiological status require close attention to the IoT-driven monitoring. For achieve this, it requires sensors to collect sensed information and uses gateways for

196 Chapter 7 connecting to the Internet and used cloud for storing the information and finally send the analyzed data through RESTful CoAP protocol for user purpose. This architecture style may helpful for fast accessing these resources and achieve better performance in data collection and analysis [26]. The revolution in health sector is quietly brewing. IoT Technology can useful for connecting billions of devices and applications using sensors, microcontrollers, and actuators. These devices may be helpful for better health monitoring and also provides more features like timely and convenient lowering costs [27e29]. In traditional the ubiquitous architecture is having mainly three divisions shown in Fig. 7.4. Those are Body Area Network (BAN), Intelligent Medical Server (IMS) and hospital system. The BAN again divided into wearable body sensor network and personal monitoring devices. Here the Internet will plays the major role to combine the IMS, BAN, and Hospital system. Every module have their own functionality to represent the health care applications. The hospital system is connected to the IMS for regular with scheduled updates of the patient health care condition.

Intelligent Medical Server Authentication Role-based Module

Real-time BAN Repository/ Context data

Data mining Decision support

Scheduled update

Internet Body Area Network • •

Wearable Body sensor networks Personal monitoring devices

Server

PaƟent informaƟon PaƟent history data

Hospital system

Medical prescription model

Figure 7.4 Traditional U-health care system architecture.

Semantic interoperability in IoT and big data for health care: a collaborative approach 197 2.3.1 Body Area Network (BAN) In this BAN, sensors are attached into body area for capture bio-signals and also includes to find out the blood pressure, pulse and breathing and body temperature. 2.3.1.1 Wireless Body Area Network (WBAN)

It is formed on basis of wearable and implantable bio-sensors in the patient’s body. These sensors may collect necessary information from patient’s body and sends to the central node by means of low frequency electromagnetic waves. 2.3.1.2 Personal monitoring devices (PMD)

These devices are like smart phones, PDAs and computers. It gets information from WSBN using Bluetooth technology or ZigBee. 2.3.2 Intelligent Medical Server (IMS) IMS receives information from BAN, and it acts as a hub between the patient and hospital. It is the backbone of entire system and is having the entire patient’s medical information. The use of agents we can easily determine the patient condition whether it is in critical or noncritical. If the condition is critical, then the data is transfer to the hospital system for giving more treatment. This can happen immediately after it is stored in the IMS system. If the condition is not critical, then the data is as usually stores in the IMS system. Luca Catarinucci et al. [26] proposed this real-time data can be deleted after a certain period of time and data stored in the IMS will be available for both doctors and staff. 2.3.3 Hospital system Data is enrolled, accessed, changed, updated, and deleted by doctors, supporting staff, and patients. The hospital staff can take the preventive or convective actions depending on the IMS output. Fig. 7.5 shows that different tiers of U-health care system IoT. In Level 0, the body sensor senses the data and it transmits to the mobile application. Mobile Application is capable of processing the data it comes from different types of IoT gateways and then compute the received information in Level 1. And also mobile application will transmit analyzed data and monitoring in Level 2. Once receive the sensing information comes through IoT gateways from cloud, mobile application will generate keywords and transmit to monitoring system through mobile application. Then the monitoring system received the keywords and gives the accurate result. The monitoring system is hosted in cloud. With the IoT, RESTful CoAP protocol and U-health care system is possible. Semantic meanings help for data filtering.

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Figure 7.5 Levels of U-health care system using IoT.

In Fig. 7.6 performs the role of RESTful CoAP protocol in U-health care systems. The patient and physician should communicate and efficient and fastest way using this protocol. The patient measures the data with sensors and RFID tags. The patient data is connected to the cloud using CoAP protocol. The other protocols like MQTT, XMPP, SOCKET, Z-wave are also used to access the IoT data stored in the cloud. CoAP is a software protocol that allows simple electronic devices to communicate over the Internet. It is designed for small devices with low-power sensors and actuators that need to be controlled or supervised remotely, through standard network interfaces. CoAP is designed for use with constrained nodes and networks. CoAP resembles with HTTP in terms of the REST model with GET, POST, PUT, DELETE methods. URIs, Response codes, MIME type etc. However, CoAP can easily interface with HTTP using proxy components, where HTTP clients can talk to CoAP servers and vice versa, which enables better Web integration and the ability to meet IoT needs. 1. Let us consider one room it is fully installed with temperature sensor network and it will be integrated with the IoT gateway. 2. The cloud/server exposes the updated information of each and every device connected to itddevice id, current status of the device, who has accessed the device last time, how many times the device has been accessed and more. 3. Then to make the connection with the cloud is implemented using web services such as RESTful CoAP protocol.

Semantic interoperability in IoT and big data for health care: a collaborative approach 199 Patient P Storage server of Sensor data from patients for future referencce

Measuring Parameters using Sensors

CoAP Protocol

Internet

Connecting server values to browser using CoAP Protocol Client requests

Physician

Figure 7.6 RESTful CoAP in IoT for U-health care system.

4. Those which are interacting with cloud through the mobile application. Requests will be sent to the cloud with the authentication and device information. Authentication is configured to ensure RESTful CoAP protocol using Datagram Transport Layer Security (DTLS). 5. Once the information is stored in cloud, it can immediately identify the IoT device through the device id and will send the corresponding request to the particular sensor network using gateways. 6. After that, the temperature sensor will read the current temperature value in the home and will send the response back as found value to the cloud. 7. Cloud will identify the user who has requested the data currently and push the requested data to the application. So user will get the current information directly on his mobile or web screen.

3. Semantic interoperability In this section totally discuss related work of SI in IoT platforms. Haslhofer and Klas [30] shown that comparative analysis and reports on metadata interoperability. Ibrahim et al. [31] proposed the SI level for traditional service oriented systems and seamless interoperable applications. Zhang et al. [32] discussed how the cloud-based infrastructures will accomplish the interoperability. Representational State Transfer [REST] as an

200 Chapter 7 architectural style to provide well-known uniform interfaces by adopting IoT platforms to achieve SI. The authors surveyed that, to gain SI in IoT platforms, mainly divided into four categorical approaches. These are illustrated as follows: 1. 2. 3. 4.

Ontologies and Standards Mapping Technologies for Data Models Data Integration and exchange systems Semantic annotations

3.1 Ontologies and Standards These are the traditional approaches to achieve SI in IoT domains. These Ontologies and standards also provide referenced data models for IoT platforms. Let us consider the approaches like GSMA [33] proposed to shows how IoT platforms and RESTful resources to manage SI producing a declarative data model. OneM2M model based on ontology [34] defines an acceptable framework for describing the semantically integrated data that have been managed in the execution of IoT-based applications. These data declarative models gave a sound reference knowledge for thinking innovative way, and to solve interoperability problems in IoT platform but solely not useful for overcome interoperability.

3.2 Mapping Technologies for Data Models Peters [35] established a mapping model for combining linguistic and terminological approaches for obtaining interoperability. The mapping data models and Technologies mainly helpful to track the information of the relation between syntax and structures. Nevertheless, mapping data models are individually specified models and resources can be constructed and integrated into vertical platforms without the flexibility to integrate external resources.

3.3 Data integration and exchange systems Lehti and Fankhauser [36] provides a data integration mechanism for supposing different types of resources in a uniform way. While Thuy et al. [37], mentioned data exchange systems to restrict the data as per the global sources way. So far, both data integration and exchange systems require highly configurable mechanisms as well as taking consideration of new data models for every heterogeneous resource has been occurred.

3.4 Semantic annotations Nowadays, these are the most powerful and popular mechanisms to achieve SI in IoT platforms. The existing data models are updates with semantic annotations on providing

Semantic interoperability in IoT and big data for health care: a collaborative approach 201 semantic labels to become model elements. Andrew et al. [38] classifies their survey on semantic annotation approaches of RESTful services from the perspective of collaboration type, vocabulary type, and structural complexity. Saquicela et al. [39] presented an approach semantic annotation using RESTful web services of cross-domain ontology technology, which will generate a good external resource for annotating IoT platforms. Even though these are good to provide external resources but still there is a limitation of semantic annotation approaches in the capture of implicit relationships between IoT platforms and resources. Khaled and Helal [27], Proposed an interoperable framework for combining RESTful and topic-based protocols in IoT environment. They mentioned that proposed ATLAS framework is taking low energy consumption for to design the IoT-based heterogeneous resources or things. Jacoby et al. [28] have proposed a framework for to deliver the data communication between the Cloud to IoT applications in an optimized and efficient manner using appropriate benchmark algorithms for providing SI. Plageras et al. [29] dealt that for embedding the things in smart home or smart building. They concluded that for analyzing and operating the things in smart home applications there is a need of big data technologies to process the high volume of data occurring from various sensor devices. In IoT based applications, the smart home concept is the emerging space in recent years. To make the things to be smart in smart home applications, available all resources in that room is connected to the Internet. Ghayvat et al. [40] proposed a smart home framework by integrating IoT devices like sensors, RFID cards, and Actuators to make resources smart using various kinds of wireless technologies. Soliman et al. [41] dealt on smart home appliances using ESP8266 platform and Arduino kit for embedding the resources wider. Lin et al. [42] convey their efforts in home automation using ZigBee technology and proposed home automation model by taking sensors and actuators. M. Antunes et al. [43] proposed an IoT-based smart home system for embedding service oriented approaches and IoT to make heterogeneous resources be semantically interconnected in smart home. Balakrishna and Thirumaran [44] have proposed a framework for semantically interoperable the smart traffic things and estimate the early stage of detection and removal of traffic conditions. They used the Raspberry Pi and MATLAB environment for semantically interconnect the traffic data. Balakrishna and Thirumaran [45] a case studyebased programming paradigms are proposed for IoT applications in an exploratory manner. Studied on ThingSpeak, ESP8266, LinkIt, RESTful CoAP protocol, and many more devices and protocols are used for their case studies. Lee et al. [46] deal that security is an emerging problem especially in IoT-based smart home application. The security issues might be consisting of physical security, authentication, confidentiality and integrity. For processing and implementing smart home application, the semantically interoperable things to be taken to the overall architecture or

202 Chapter 7 framework. Babovic et al. [47] have studied about the web performance standards and protocols for evaluating the IoT applications. It also tokenized that their experimental results are more convincible and used for predicting the web performance on connecting a semantic interoperable IoT smart things across the world. A sound number of frameworks and architectures are studied under literature survey. However, these all are not meet the needs of the semantic scholars and users. So, in this chapter proposed a new and innovative SI framework using RESTful resources and ontology-based matching and reasoning algorithms for smart health care applications. Toward a merging combination of IoT and big data technologies is always give a better result in terms of efficient, reliable, scalable, and secure compare to the state of the art schemes.

4. Semantic interoperability in IoT health care In this proposed research work, to achieve SI between patients and physicians, a RDF is used among heterogeneous IoT resources. The authors has analyzed health care dataset on SI model based on IoT. The health care dataset is in subject, predicate and object format. It consists of RDF triples format to expose the health care data. The integration of all the health care records in an efficient and semantic manner is difficult. Firstly, to annotate the records in health care dataset RDF framework is used. Secondly, to extract the information from anytime from cloud, SPARQL queries is required. This framework should consist of three modules: user interface (UI) module, SI module, and cloud service module. In UI module, the patients view the IoT devices generated data and physicians may supervise the IoT devices data. The SI module is openly communicated with the UI module. In SI, interoperability between IoT devices and various merchants is a critical task. SI is the term used to exchange the data with descriptions and meanings in an understandable way. This SI module works on different merchants and IoT resources to be interoperable. It consists of the semantics in IoT data including self-described descriptions. First, the IoT resources can take the data from a UI section, thereafter adding the semantic annotations to the IoT devices data. Then interoperable the things more easily with meanings and shared vocabulary.

4.1 Adding semantic annotations to the IoT health care data The sensors and actuators plays a key role to communicate the heterogeneous IoT devices. Each and every IoT device having their sensors API, is used to annotate the data as per their respective domain. Then this annotated data is sent to the IoT sensors API, it offers

Semantic interoperability in IoT and big data for health care: a collaborative approach 203

Figure 7.7 Use case scenario of Semantic Interoperability (SI) in IoT health care [48].

the sensors may communicate with the whole world. Sensor Web Empowerment (SWE) framework is used to provide web services to the network for accessing and discovering the IoT devices data. The keywords are used here to search the health care data and it semantically annotate the human diseases. Then the health care data set is transfer to the next module SI where each attribute is categorized to their respective domain of human diseases. The attributes with its meaningful descriptions has been sent to the classified diseases part. In this, the type of human disease is classified to find out which type of patient has suffering with which type of disease. Fig. 7.7 shows the use case scenario of SI inn health care systems to annotate semantically from patients to their appropriate diseases. Once the disease has identified then the system may allow the suitable medicine to that particular disease. Both ends mean disease and medicine is matched then the doctor given the right medicine, otherwise the physician may suggested the wrong medicine. The intelligent cloud attached with storage, it stores all the health care data and it clarifies whether the patient and physician identified information is correct or not. Thereafter, the identified diseases in various categories as per their health care sector are sent to the Taggling system, where diseases are semantically connecting manually or automatically using Resource Description Framework (RDF). The SPARQL queries are managed to extract the health care data stored in the cloud. The following SPARQL query 1 is the sample query shows that how to extract the patient information in subject, object and predicate manner.

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select ?s ?p ?o where { ?s ?p ?o . } limit 100

SPARQL query 1 Where s, p, and o are the subject, predicate and object respectively. In above query the limit is 100. It means that the query can extract the maximum of 100 triples will extract. The maximum of limit is based on the maximum of triples stored in the taken health care data set. The patients and physicians were communicated more conveniently with semantical meanings using RDF framework. The SPARQL queries can extract the patient information quickly. Irrespective of distance and time the patients may communicate with physicians and observe patient diseases remotely.

4.2 Experiments and results The experiments are carried out on heart disease dataset. It contains the various types of heart diseases of 125 patients. Fig. 7.8 shows the patient wise graphical representation with detailed information. The generated graph shows on x-axis as patient attributes like age, sex, id and type etc., and y-axis on dataset measurement values. The patient sex attribute contains value one for male and o for female. The other attributes values one describes yes and o describes not. The patient age attribute indicate the age between the dataset is likely from 60 to 67 years. The attribute Tpeakbpd has the highest value it means that all patients’ bp value is maximum for heart disease patients. By observing the generated graph, some attributes can also have negative values. It indicates that these attributes are not included in during this experiment. The tableau tool was used in this experiment to extract the patient wise attributes against their heart diseases. The patients accumulated data ids are plotted on horizontally and their corresponding values are plotted at vertically. The dataset contains the 14 attributes for representing the patient diseases. Fig. 7.9 depicts that the total reflection of heart disease dataset. It evaluated the results obtained from the heart disease dataset is functionally generated result is correct or not is identified. The graph contains the different colors to represent the patient data. The values both positive and negative. The positive values are placed on above zero location and negative values are placed below the zero values. The patient id 4006 shows that all patients having diagnosis most accurate results. Some other patient ids like 406, 4063 and 3014 are generated negative results, shown that bp values not included during the

Semantic interoperability in IoT and big data for health care: a collaborative approach 205

Figure 7.8 Heart disease graph representation based on patient Id [48].

experiment. The patients of ids 4056 and 4023 have diagnosis and almost all highest peak values maintained thought the experiment. The cigs represents cigarettes used by patients per day basis is negative denotes that not included and not analyzed during the experiment. Fig. 7.10 is the RDF graph extracted from patient’s heart diseases data set using gruff tool. Fig. 7.11 shows the individual node interacts with the other available nodes.

5. SI in big data health care The proposed model is little bit same as the previous proposed model. The only difference is that here adds the big data analytics (BDA) for analyzing the patients’ health care data. In this model, mainly consists of three sections: (1) UI, (2) SI, and (3) Intelligent cloud with BDA. As usual, the UI section can be interacted with patients and physicians through supportable IoT devices. These IoT devices are used for sensing the health care data for patients and used to physicians to monitor the patient health condition remotely. In next section SI is directly concerning and deals with UI design. The UI section can take

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Figure 7.9 Heart disease diagnostic measurement values [48].

Figure 7.10 Semantically interoperable RDF graph.

Semantic interoperability in IoT and big data for health care: a collaborative approach 207

Figure 7.11 Semantically interoperable RDF graph chunks.

patients’ health care data through IoT devices and it sends it to the SI phase. In this phase the incoming data from IoT devices is make more meaningful to the patients. By adding semantics with health data and exchange the health information conveniently. For that, the semantically annotated data may consist of the consumer and producer. The producer acts as a data provider while the consumer may treated as data user. These two can interact with each other using semantic mediator. The semantic mediator role plays a key role here. The data provider is a sensor model used to observe the data in three dimensionsprocessor, aggregator and archer. The flow of interaction among producer and consumer is as follows: the data provider is using semantic provider and builds the vocabulary then send to the ontology registry for registering the sensed semantic data. The semantic mapping builder can interact with semantic provider and ontology registry for provide semantic data. The semantic reasoning can take the semantic data and send to the consumer (data user) for visualize and monitoring health data. The SI phase concerns and deals with intelligent health cloud, BDA and online storage. The intelligent health cloud can store the semantically annotated data that comes from SI phase. The doctor and patient information stores in this database. The various types of health care data like food and nutrition databases, diseases-specific tips databases, and health care service databases are maintained. The BDA is a data science approach for performing analysis on intelligent health cloud data. The BDA can takes the dataset stored in cloud and pulls the understandable semantic interfaces from IoT sensor health data. These approaches are

208 Chapter 7 available on low cost and fast cess support to manage the data processing. The analytics can propagate the pattern matching algorithms in data processing to make the IoT raw data more sensible and quick decisions. RDF is a meta data model mainly used to expose the IoT data with more descriptions. The SPARQL is helpful to drag the hidden patterns of the health data from semantically annotatable data the BDA approaches are performed on IoT collected data originated from various IoT devices through sensors and actuators and then merge the semantic annotations to deliver the IoT data more descriptive, meaningful, costeffective and quick-decision.

5.1 Adding semantic annotations to the big data health care data The sensors and actuators plays a key role to communicate the heterogeneous IoT devices. Each and every IoT device having their sensors API, is used to annotate the data as per their respective domain. Then this annotated data is sent to the IoT sensors API, it offers the sensors may communicate with the whole world. SWE framework is used to provide web services to the network for accessing and discovering the IoT devices data. The keywords are used here to search the health care data and it semantically annotate the human diseases. Then the health care data set is transfer to the next module SI where each attribute is categorized to their respective domain of human diseases. The attributes with its meaningful descriptions has been sent to the classified diseases part. In this, the type of human disease is classified to find out which type of patient has suffering with which type of disease. Fig. 7.12 shows the use case scenario of SI in health care systems to annotate semantically from patients to their appropriate diseases. Once the disease has identified then the system may allow the suitable medicine to that particular disease. If the disease and medicine is matches then the doctor given right medicine otherwise the physician may suggested the wrong medicine. The intelligent cloud attached with storage, it stores all the health care data and it clarifies whether the patient and physician identified information is correct or not. Thereafter, the identified diseases in various categories as per their health care sector are sent to the Taggling system, where diseases are connected manually or automatically using RDF.

5.2 Experiments and results To evaluate the SI sue scenario in big data health care gruff tool was used to represent the health data in graphical way. Two big and combined datasets have taken to perform analysis for big data SI in exchange of information from patient to physician in an effective way. The drug side effects data base item was taken from SIDER cloud platform and it has to be transformed into RDF

Semantic interoperability in IoT and big data for health care: a collaborative approach 209

Figure 7.12 Use case scenario of semantic interoperability (SI) in big data health care [49].

graph. Using MedDRA dictionary, the drugs to representation of side effects have been enriched as well as extracted. The MedDRA repository can be performed more results to implement the RDF schema. The drug dataset having 212,154 triples along with their side effects. In second data set, the drugs with diseases have taken from MedExpert. It contains the 896 triples to represent the drugs with diseases in subject, object and predicate manner. The proposed use case scenario is useful to the patients to choose their suitable correct medicine for their diseases. The IoT devices taken the patient health care data coming from sensors stored in the intelligent health cloud platform for performing analysis and visualizations. The SPARQL queries are used to interact and extract with the big data sets by taking MedDRA repository search items. To extract the patient drug side effects dataset for analysis and visualizations, first it has to be transform into RDF format to enable the health care data semantically interoperable and readable. The SPARQL query language is used to extract and enrich the hidden patterns stored in the health data base. The SPARQL query 2 is used to extract unique drugs and their values from drugs side effects dataset.

210 Chapter 7 select ? heart attack ? Value where { ? heart attack ? Value. limit 100 }

SPARQL query 2 Fig. 7.13 shows the resulted graph for the above SPARQL query 2. The drugs side effects are extracted and unique triples are visualized from RDF database to make data semantically transferable. Using MedDRA dictionary, the drugs to representation of side effects have been enriched as well as extracted. The MedDRA repository can be performed more results to implement the RDF schema. The drug dataset having 212,154 triples along with their side effects. Select ? Row ID ? Member ? Year ? Number of diabetes deaths where { ? Row ID ? Member ? Year ? Number of diabetes limit 100 }

SPARQL query 3

Figure 7.13 Unique attributes from drug data class using SPARQL query.

Semantic interoperability in IoT and big data for health care: a collaborative approach 211

Figure 7.14 Concept types and attributes of MedDRA repository.

The drugs side effects database consists of large size of attributes around the taken big data set contains 111,752 triples. So in this, some attributes are functionally validated and some other attributes are not validated accurately. The role of SPARQL is highly inflammable to enrich the all attributes must be validated from MedDRA repository. The SPARQL query 3 is as shown above used to find out the drug types and attributes of the patient. Fig. 7.14 shows the resulted output for the query 3. The arrow in output depicts that the MedDRA concept type. Moreover, the drug side effects data set consists of attributes type as PT, LLT, and null literal values as categories. Select ? Value ? predicate where { ? Value ? predicate. limit 100 }

SPARQL query 4 The SPARQL query 4 has been performed to find out the property of the attributes of the drug data set. The linkage of the database connections and drug side effects have evaluated for easier representation to the users. Fig. 7.15 shows the resulted output of the query 4, it consists of the both attribute values and predicates of the drugs data with their side effects. The arrow in output depicts that the MedDRA concept type, range, comment, domain and label. Moreover, the drug side effects data set consists of predicates as frequency, string, attribute, property and null literal values as categories. Using MedDRA dictionary, the drugs to representation of side effects have been enriched as well as

212 Chapter 7

Figure 7.15 Linked data drugs side effects of property attributes.

extracted. The MedDRA repository performed more results to implement the RDF schema. Fig. 7.16 shows the semantically interoperable Big data chunks of drugs with diseases. In Table 7.4 shows that the all available implementation tools, supporting languages and available URLs are placed respectively. These all are the needed information for users to implement the SI in healthcare domains. Table 7.5 depicts that the all available additional implementation tools, storage area, SPARQL support, SPARQL support and update, SPARQL protocol support and native APIs are placed respectively. These all are the needed information for users to implement the SI in healthcare domains.

6. Conclusion and future work In this chapter the authors have studied the SI in IoT and big data for health care domain. SI is used to exchange the information from one place to another place in an efficient and meaningful way. The data is generated from various heterogeneous devices, communication protocols, and data formats that are enormous in nature. This is a significant problem for IoT application developers to make the IoT generated data interoperable. In the existing approaches there is lack of well-defined standards and established tools to solve SI problem in IoT and big data applications. This chapter discussed a collaborative approach to address the SI in IoT and Big data for health care applications. In the health care domain, the physicians and patients may interoperable one

Semantic interoperability in IoT and big data for health care: a collaborative approach 213

Figure 7.16 Semantically interoperable Big data chunks of drugs with diseases.

to other for their functioning in an effective way. Both IoT and big data are dominant technologies for health care applications. This chapter mainly dealt with two use cases namely (1) IoT in health care systems and (2) BDA in health care systems. This chapter summarized with supporting SI tools and developing methodologies in both IoT and BDA technologies for health care applications. The two use case approaches were used for SI in health care systems. Gruff and Tableau tools were used for performing experiments and analysis on health care data. First use case dealt with SI in IoT health care data. In this use case, how the patients and physicians was communicated in health care using semantic annotations. The RDF was used for extract the health care data using URI’s to relate the

214 Chapter 7 Table 7.4: SI implementation tools. S. No

Name of the tool

Language used

1

3store

C

2

AllegroGraph

Common Lisp

3

AnzoGraph

C/Cþþ

4 5 6 7 8 9 10

ARC2 Blazegraph BrightstarDB Cayley ClioPatria Dydra Enterlab SimpleGraph

PHP Java C# Go SWI-Prolog, C Lisp, Cþþ Java

11 12

gStore GraphDB by Ontotext

Cþþ Java

13 14 15 16 17 18 19 20

Halyard IBM DB2 Apache Jena KiWi MarkLogic Mulgara Nitrosbase OntoQuad RDF Server

Java Java, SQL Java Java Cþþ Java Cþþ Cþþ

21 22 23

OpenAnzo OpenLink Virtuoso Oracle

Java C Java, PL/SQL, SQL

24 25

Parliament Pointrel System

Java, Cþþ Java, Python

26

Profium Sense

Java

27

RAP

PHP

28

RDF::Core

Perl

29 30 31 32 33 34 35

RDF::Trine RDF-3X RDFBroker RDFLib Redland RedStore Apache Rya

Perl Cþþ Java Python C C Java

URL sourceforge.net/projects/ threestore/ www.franz.com/agraph/ allegrograph www.cambridgesemantics.com/ product/anzograph/ github.com/semsol/arc2/wiki www.blazegraph.com brightstardb.com cayley.io cliopatria.swi-prolog.org dydra.com github.com/enterlab/ simplegraph github.com/Caesar11/gStore ontotext.com/products/ graphdb github.com/Merck/Halyard pic.dhe.ibm.com jena.apache.org marmotta.apache.org/kiwi www.marklogic.com www.mulgara.org nitrosbase.com www.ontos.com/products/ ontoquad/ www.openanzo.org virtuoso.openlinksw.com www.oracle.com/technetwork/ database parliament.semwebcentral.org sourceforge.net/projects/ pointrel www.profium.com/ technologies/profium-sense www4.wiwiss.fu-berlin.de/ bizer/rdfapi metacpan.org/module/RDF:: Core metacpan.org/pod/RDF::Trine code.google.com/p/rdf3x rdfbroker.opendfki.de github.com/RDFLib/rdflib librdf.org www.aelius.com/njh/redstore rya.apache.org

Semantic interoperability in IoT and big data for health care: a collaborative approach 215 Table 7.4: SI implementation tools.dcont’d S. No

Name of the tool

Language used

36 37

Semantics Platform SemWeb-DotNet

C# C#

38 39

Sesame/RDF4J SiDiF- Triplestore

Java Java

40

Smart-M3

Python, Java, C, C#

41 42 43

Soprano Stardog StrixDB

Cþþ Java Cþþ, Lua

44

Wukong

Cþþ

URL www.intellidimension.com github.com/JoshData/semwebdotnet rdf4j.org github.com/BITPlan/org.sidif. triplestore sourceforge.net/projects/smartm3 soprano.sourceforge.net stardog.com sourceforge.net/projects/ strixdb/ github.com/SJTU-IPADS/ wukong

Table 7.5: Additional SI implementation tools.

S. No

Tool support

Storage area

Native SPARQL

Native SPARQL/ Update

Native SPARQL protocol

Native APIs

1

4store

Triplestore

Yes

Yes

Yes

2

AllegroGraph

Graph

Yes

Yes

Yes

3

AnzoGraph

MPP In-memory Triplestore

Yes

Yes

Yes

4 5 6

ARC2 ARQ BrightstarDB

Yes Yes Yes

Yes Yes No

Yes

7

CM-Well

Yes

Yes

No

8 9 10

Corese D2R Server Dydra

3rd party 3rd party Graph data model in Heap file Apache Cassandra 3rd party 3rd party Graph database in the cloud SaaS

Command line only For most modern programming languages For programming languages that support gRPC bindings PHP Java .NET Framework or Web Service Java, Scala,

Yes Yes Yes

No Yes Yes

No Yes Yes

Java Java REST API

No

Continued

216 Chapter 7 Table 7.5: Additional SI implementation tools.dcont’d

S. No

Native SPARQL

Native SPARQL/ Update

Native SPARQL protocol

Tool support

Storage area

11

GraphDB by Ontotext

Triplestore/ Quadstore

Yes

Yes

Yes

12 13 14

Apache HBase Object-relational 3rd party

Yes No Yes

Yes No No

No No No

15 16 17

Halyard IBM DB2 Intellidimension Semantics Platform 2.0 Jena KAON2 MarkLogic

Tuple store 3rd party Triplestore/ Quadstore

Yes Yes Yes

Yes No No

Yes No Yes

18

Mulgara

3rd party

Yes

19 20

OntoBroker OntoQuad RDF Server

Triplestore Triplestore/ Quadstore

Yes Yes

Yes Yes

Yes Yes

21

Open Anzo

3rd party

Yes

No

Yes

22

OpenLink Virtuoso

Hybrid (relational tables and relational property graphs)

Yes

Yes

Yes

23

Oracle DB Enterprise Ed.

Object-relational

No

No

No

Native APIs Java (Jena and RDF4J (Sesame)) Java Java .NET Framework Java Java REST API, SPARQL Endpoint, Graph Protocol Endpoint, Java API, XQuery, SQL/ODBC Java or REST API Java Java, SPARQL Endpoint or REST API Java, JavaScript, .NET Framework ODBC, JDBC, ADO.NET, OLE DB, XMLA, HTTP, etc., serving most modern programming languages including C, PHP, Perl, Python, Ruby, Java, JavaScript, .NET Framework, etc. For most modern programming languages Continued

Semantic interoperability in IoT and big data for health care: a collaborative approach 217 Table 7.5: Additional SI implementation tools.dcont’d

S. No

Tool support

24 25 26 27

Parliament Pellet Pointrel Profium Sense

28

RAP

29

RDF API for PHP RDF-3X

Storage area

Native SPARQL

Native SPARQL/ Update

Native SPARQL protocol

Native APIs

3rd party 3rd party Triplestore In-memory triplestore In-memory triplestore or heap file 3rd party

Yes Yes No Yes

Yes No No No

Yes No No Yes

Java or Cþþ Java Python Java

Yes

No

No

PHP

Yes

No

No

PHP

Triplestore

Yes

No

No

3rd party 3rd party 3rd party

Yes No Yes

Yes No Yes

Yes No Yes

34

RDF::Query RDFBroker Redland, Redstore SemWeb.NET

Command line only Perl Java C

3rd party

Yes

No

Yes

35 36 37

Sesame Soprano SparkleDB

3rd party 3rd party Triplestore/ Quadstore

Yes No Yes

Yes No Yes

Yes No Yes

38

SPARQL City

Triplestore

Yes

Yes

Yes

39 40 41 42 43

SPARQL Engine Stardog StrixDB Twinql Wukong

3rd party Triplestore Triplestore 3rd party Graph

Yes No Yes Yes Yes

No No Yes No No

No No Yes No No

30 31 32 33

.NET Framework Java Cþþ For most modern programming languages Command Line, Web Interface Java Java, Groovy Lua Lisp Command line only

IoT things generated data semantically. The SPARQL quires used for visualize the semantic data in subject, object and predicate manner. In second use case, SI in health care using BDA was studied. Here analytics is a data science mechanism plays key role for fast processing and semantic inferring from IoT raw data. The data analytics was performed well under any circumstances. The patients and physicians were communicated more conveniently with semantical meanings using RDF framework. The SPARQL queries can extract the patient information quickly. Irrespective of distance and time the patients may communicate with physicians and observe patient diseases remotely.

218 Chapter 7 The authors have discussed only SI in health care platforms using two case studies. In future, the authors can progress the work as follows 1. SI in health care domain both IoT and BDA implemented using appropriate tools. 2. Syntax interoperability is also considered as a major impact on health care sector whether the health data is syntactically correct or not. 3. The health data is stored in intelligent health cloud platform, so providing security to the stored health data is a critical problem. 4. Semantic analytics is also a major area to do more research for classification and clustering on IoT smart data.

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Semantic interoperability in IoT and big data for health care: a collaborative approach 219 [14] D.D. Sanju, A. Subramani, V.K. Solanki, Smart city: IoT based prototype for parking monitoring & parking management system commanded by mobile App, in: Second International Conference on Research in Intelligent and Computing in Engineering, 2017. [15] R. Dhall, V.K. Solanki, An IoT based predictive connected car maintenance approach, International Journal of Interactive Multimedia and Artificial Intelligence 4 (3) (2017). ISSN 1989-1660. [16] V.K. Solanki, M. Venkatesan, S. Katiyar, Conceptual model for smart cities for irrigation and highway lamps using IoT, International Journal of Interactive Multimedia and Artificial Intelligence 4 (3) (2018) 28e33. ISSN 1989-1660. [17] V.K. Solanki, M. Venkatesan, S. Katiyar, Think Home: A Smart Home as Digital Ecosystem in Circuits and Systems, Scientific Research Publishing Inc, 2018. ISSN 2153-1293, Vol. 10, No. 07. [18] V.K. Solanki, S. Katiyar, V.B. Semwal, P. Dewan, M. Venkatesan, N. Dey “Advance Automated Module for Smart and Secure City” in ICISP-15, Organised by G.H.Raisoni College of Engineering & Information Technology, Nagpur, on 11e12 December 2015, Published by Procedia Computer Science, Elsevier, ISSN 1877-0509. [19] A. Yachir, B. Djamaa, A. Mecheti, Y. Amirat, M. Aissani, A comprehensive semantic model for smart object description and request resolution in the internet of things, Procedia Computer Science 83 (2016) 147e154. [20] P.P. Jayaraman, C. Perera, D. Georgakopoulos, S. Dustdar, D. Takker, R. Ranjan, Analytics-as-a-service in a multicloud environment through semantically-enabled hierarchical data processing, SoftwaredPractice and Experience 47 (3) (2016) 1139e1156. [21] P. Buneman, S. Staworko, RDF graph alignment with bisimulation, Proceedings of the VLDB Endowment 9 (12) (2016) 1149e1160. [22] W. Zheng, L. Zou, W. Peng, X. Yan, S. Song, D. Zhao, Semantic SPARQL similarity search over RDF knowledge graphs, Proceedings of the VLDB Endowment 9 (11) (2016) 840e851. [23] S. Cirani, M. Picone, L. Veltri, CoSIP a constrained session initiation protocol for the Internet of things, in: European Conference on Service-Oriented and Cloud Computing, Springer, 2013, pp. 13e24. [24] P. Kayal, A Comparison of IoT Application Layer Protocols Through a Smart Parking Implementation (MS thesis), North Carolina State University, 2016. [25] F. Paganell, S. Turchi, D. Giuli, A web of things framework for RESTful applications and its experimentation in a smart city, IEEE Systems Journal 10 (4) (2016). [26] L. Catarinucci, D. De Donno, L. Mainetti, L. Palano, L. Patrono, M.L. Stefanizzi, L. Tarricone, An IoTaware Architecture for Smart Healthcare systems, IEEE Internet of Things Journal 2 (6) (2015) 515e526. [27] A.E. Khaled, S. Helal, Interoperable communication framework for bridging RESTful and topic-based communication in IoT, Future Generation Computer Systems 9 (2) (2019) 628e643. [28] M. Jacoby, A. Antonic, K. Kreiner, R. Lapacz, J. Pielorz, Semantic interoperability as key to IoT platform federation, LNCS 10218 Interoperability and Open- Source for the Internet of Things (2017) 3e19. [29] A.P. Plageras, K.E. Psannis, C. Stergiou, H. Wang, B.B. Gupta, Efficient IoT- based sensor BIG data collection-processing and analysis in smart buildings, Future Generation Computer Systems 8 (2) (2018) 349e357. [30] B. Haslhofer, W. Klas, A survey of techniques for achieving metadata interoperability, ACM Computing Surveys 42 (2) (2010) 1e37. [31] N. bin M. Ibrahim, B. Hassan, M. Fadzil, A survey on different interoperability frameworks of SOA systems towards seamless interoperability, in: 2010 International Symposium on Information Technology (ITSim), vol. 3, 2010, pp. 1119e1123. [32] Z. Zhang, C. Wu, D.W. Cheung, A survey on cloud interoperability: taxonomies, standards, and practice, ACM SIGMETRICS - Performance Evaluation Review 40 (4) (2013) 13e22. [33] GSMA, IoT Big Data Harmonised Data Model, 2016. [34] OneM2M Technical Specification, Base Ontology, 2015. [35] W. Peters, Establishing Interoperability between Linguistic and Terminological Ontologies, 2013, pp. 27e42.

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CHAPTER 8

Why big data, and what it is: basics to advanced big data journey for the medical industry Meena Moharana, Manjusha Pandey, Siddharth Swarup Routaray School of Computer Engineering, KIIT University, Bhubaneswar, India

Chapter Outline 1. Introduction 222 2. Why big data? 223 2.1 Application to medical industry 224 2.1.1 Big data in a medical domain 224 2.1.2 Electronic health records 224 2.1.3 Real-time alerts 225 2.1.4 Evidence-based medicine 225 2.1.5 Hospital readmissions 225 2.1.6 Fraud detection 225

3. Health care and the four Vs of big data 225 4. An architecture of large-scale platform to develop a predictive model 230 4.1 Types of big data 230 4.1.1 Primitive big data 230 4.1.2 Nonprimitive big data analytics 231 4.2 Platform to big data 231 4.2.1 The Hadoop Distributed File System (HDFS) 4.2.2 Map Reduce 231 4.2.3 PIG and PIGLatin 232 4.2.4 Jaql 232 4.2.5 HBase 232 4.2.6 Cassandra 232 4.2.7 Avro 232 4.2.8 Hive 232

5. The model through big data analytics

231

232

5.1 An architecture of large-scale platform to develop a predictive model 5.1.1 Map Reduce (Map and Reduce) 234 5.2 Functional network algorithm 235

233

Handbook of Data Science Approaches for Biomedical Engineering. https://doi.org/10.1016/B978-0-12-818318-2.00008-8 Copyright © 2020 Elsevier Inc. All rights reserved.

221

222 Chapter 8 5.2.1 Functional network (n/w) can be learned by the use of one of the optimization techniques

6. Impact of big data

236

6.1 Examples to complex biomedical information 6.1.1 Dell health care solutions 237 6.1.2 IBM health care and life sciences 237 6.1.3 Intel health care 238 6.1.4 Amazon web services 238 6.1.5 GE health care life science 238 6.1.6 Oracle life sciences 238 6.1.7 Cisco health care solutions 238 6.2 Personalized medicines 239

7. Ethical issues

235

237

239

7.1 Ethical themes 240 7.1.1 Consent 240 7.1.2 Data protection 242 7.1.3 Privacy 243 7.1.4 Ownership 243 7.1.5 Epistemology 244 7.1.6 Objectivity 245

8. Conclusion 246 References 246 Further reading 249

1. Introduction Every experiment has a cause. Each evolution has someone’s involvement and each requirement tends to a new invention. Today, data science is at the core of each and every engineering and nonengineering area of study. Most of the engineering and nonengineering organizations are besieged by digital technology that leads to early research and development. Because American higher education has been at the fore front of digital technology, it requires too many inventions into the classrooms, laboratories, and eventually administration. Big data [1e3], which is a large, voluminous data storage system that replaces a traditional database system along with data storage, techniques of retrieval of valuable information, and all managing techniques on data. Since the 1950s, the core of every digital requirement is data. Whatever may be the question and solution to the particulars, well arranged data is required. The problem arises when engineers, corporations, or any organizations deal with large data, because difficulties arise while storing large voluminous data in the form of rows and columns or in any storage device. Many times, while proceeding over the large data, processing speed and storing capacity have been reduced in traditional approach compared to big data analytics. The phrase, “every problem calls for new solution,” and big data [4] is the solution to those problems

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that arise while going for traditional data analysis. It collects a large amount of data from various data repositories or warehouses, different social media, internal and external data source to the organization, recorded data in the form of information in medicals, school and colleges, different firms and companies, etc. In the context of big data, which is considered as a large voluminous database, the rate of collection of data is very high as compared to the traditional method. The velocity of data collection is very high. Data collected for analysis, experiment, or any research work are of different formats. They may be text, numeric value, or graphics and also are collected in different format. Big data analytics [5] not only bounded by the techniques of collection, retrieval or storing the data, it explores the techniques of traditional data storage system or traditional database system. New thinking, new ideas or a procedure, that has been basic root to an experiment.

2. Why big data? Life science deals with living things or biological structures that mete out with huge data and termed as data science [6] accelerated by data-driven technology. In the medical era, researchers and scientists have depended on data related to the science of different fields. There are different branches in data science, and data are collected in such a manner that data from each branch are related to the others. Each section of research gathers a huge amount of data for its internal and external use. Highly innovated laboratories and highthroughput analytics techniques generate large amounts of data stored in a large-scale dimensional database. Data related to the medical domain is different from data of other fields, and collection of data depends on the structure, intersection of genes, structure of protein crystals, gene-expression measurements, and also phenotype studies create a large data and have submitted to gene bank. Data volume regarding health care systems increase with dramatic growth. Reimbursement data model related to health care has changed accordingly. In a running year, it is the basic need of a health care organization [7] not only to adopt the techniques which are advanced or latest in accordance to profit or private motivator but also to adopt the latest technology. Big data [8] technique should be leveraged to infrastructure, tools, and techniques and have the potential to loose the level of risk at millions of dollars in to profit and revenue. Revealing the DNA polymorphisms [9], research has been retaining to the tip of the iceberg which generates much effort and is economically more expensive to the engineering researcher [10] or biomedical scientist. It creates the ability of doing research in locating, integrating, and accessing such type of data. It also faces the challenges by a growing cadre of biologists termed “biocurators.” The biocuratorsditself a buzzword that controls over raw biological data, retrieve the valuable information from the different literature related to medical science that have already published or yet to be publish and develop a structure to map data and information for the availability of knowledge through online.

224 Chapter 8

2.1 Application to medical industry The quantity of data, either structured or unstructured, comes from different sources. Big data analytics comes with challenges such as storing, analyzing, searching, querying, and updating of data along with maintaining privacy, which was not adequately maintained by the traditional systems. Big data exhibits the four Vs, i.e., volume, variety, velocity, and veracity [11]. Big data has opened a door to make the world smarter. Below are some of the big data use cases changing the world of health care. Fig. 8.1 depicts different fields of medical domain. 2.1.1 Big data in a medical domain The health care industry is one of the fascinating areas where big data is seeded or implemented profitably in shaping the data collected clinically. Big data overcome the traditional technique by applying its advanced data analytical tools by which it will helpful to both clinicians for the treatment of patient and also to the researcher for doing the research. There are varieties of data relying to medical science needs proper treatment. All data has its uniqueness and posses a certain function to be executed. If not properly maintained, may leads to wrong solution. 2.1.2 Electronic health records Patient biological records or health history details have been stored traditionally in files and folders. Sometimes it leads to erroneous information while keeping the data and leads to wrong prediction about patient disease. Electronic health record (EHR) [12] is one of the widespread methods to keep the patient details using big data techniques. EHRs keep the details of each patient’s health chart and their medical reports, which helps in reducing the need for duplicity in tests and the associated cost.

Electronic health records

Real time alerts

Evedence based medicines

hospital readmission

Fraud detection

Figure 8.1 Different fields of medical domain.

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2.1.3 Real-time alerts Clinical support decision is a real-time application. It would offer prescription after analysis of the medical data of a patient. This will help the doctors to analyze their patient’s health conditions and suggest the precaution. If a patient is suffering from any disease, for example, blood pressure issues or a headache, then a sudden increase or decrease of blood pressure or any health issues related to the disease will be analyzed by their concerned doctor and provides the information with appropriate treatment. All the procedure of treatment be carried out by latest techniques of big data. 2.1.4 Evidence-based medicine Evidence-based medicine provides the doctor with information about the patient’s record and also compares the symptoms that have been stored in a larger database of the clinical data, for which it will be easy for enabling accurate, faster, and more efficient treatments. It is one of the use cases of big data helps in easy decision making. 2.1.5 Hospital readmissions Big data analytical techniques identify the at-risk patients on the basis of their medical reports, records, and clinical reports and offers them a reduced readmission rate that will help in allowing a patient not to focus on the readmission charges but on their clinical treatment. 2.1.6 Fraud detection While keeping the observation of different test of patients, keeping their record of health issue it is necessary that all the records or information related to each patient should maintain privacy because each data are unique. Big data analytical technique helps in dealing with fraudulence in the billing, personal identity, patient records, clinical test etc. Insurance fraud has become a national problem where claimants try to obtain money and they use big data techniques to help the patients to prevent them from fraud. They keep on changing the database for security point of view and therefore the insurance company regularly maintains the updating through predictive analysis which plays a crucial role in security concerns. Huge chunks of changing data are maintained and secured using this big data. Big data analytics technique and its use cases are growing day by day, which helps the different firms and organizations, whether it is social media or health care. It helps in reducing work effort and memory space by gaining high productivity and growth, innovative ideas, reduced time and cost associated to it.

3. Health care and the four Vs of big data The analytics that accelerate the growth of data in health care is prodigious. The capability to handle the data that has been created or generated are characterized by the four Vs of

226 Chapter 8 big data analytics: velocity, veracity, volume, and variety [11]. Big data analytics store the large volume of data relating to data science that is being generated at a very high speed every second. Medical science data is generated, collected, stored, and managed with the help of four Vs of data analytics. Data in health care may be genomics structure, the state of infection, symptoms of a particular disease of any patient, history record of any disease of any particular patient, history record of any observations to structural changes of human body, and a variety of data which has been generated and sequentially kept. The data that has been kept and stored in a large data storage area is considered its volume of data. The amount or the quantity of data is known as its volume. Most of the medical data structured or unstructured are thus collected and stored electronically. As in any good system, it is necessary that the data collected must be sufficient or as enough as one can develop a link up between patient and health care structure. Because more the information and the fare history of patient record, best will be the treatment procedure. Maintaining the fare history record regarding patient health condition that leads to develop or it will be beneficiary to deal with a good medical system. Either through EMR or EHR [13e16], medical data has been collected. EMR [16] is a way of keeping the data digitally rather than keeping and maintaining the data through traditional, analog, ways for each individual patient. The technique has been characterized by one of the four Vs of Big data, i.e., volume, which means the amount of data gathered that relates to the respective domain. The data regarding patient health history are focused on the genomic structure [17], harmonic diseases records, recorded history of particular diseases to a particular patient parental connectivity in terms of genes. Not only does big data deal with the structures related to medical domain but also to its deferent substructures of a particular domain. Likewise, substructure deals with huge data and with the different features of big data; it is possible to manage with proper maintenance of records and leads to the best service for the medical customer (patient, drug supplier, sanitation maintainer, outsider/insider visitors, staff). How much data has been collected in the instance of volume stored by applying the various techniques or methodology of big data, from which is easy to access or retrieve the valuable data. Fig. 8.2 shows the schematic representation of EHR. In each and every second of life, the data increases at a huge rate. It requires large amounts of data storage to keep track of all the records related to each and every branch of a well-managed hospital system. Big data comes into play by aggregating or collecting more information regarding medical issues. Currently, the major problems come from (1) lack of domain knowledge; (2) insufficient knowledge regarding technical issues; (3) presence of inefficient technical and nontechnical people who deal with or are linked with wholesome project. From a medical point of view, insufficient knowledge about pharmaceutical products or the pharmaceutical

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Patient ID with registration details Order of clinical treatment & OPD visit summary

Pathology data EHR

Classification and indexing of diseases

Scanned document of previous and recent medical data

Figure 8.2 Schematic representation of EHR.

industry and inadequate knowledge regarding biological issues some times lead to wrong inventions of medicines. Big data analytics provide a vast platform to deal with such problems, by collecting and analyzing over data. The volume of data thus collected, if not properly managed, will present problems at the same point even after applying the latest ongoing technology. If it is taken into consideration in medical data science, data analytics submerged more and more voluminous data with multiple scales for what exactly a diseases constitutes, like the diseases from DNA, lack of proteins, disorders in cells and tissues, organisms, and organs [17]. These are the scales of medical science that can be overwhelmed and bring in a new direction to medical data science for solving so many unsolved problem issues. While approaching any such technique or methodology for the advancement of research that presents problems, it must require to anatomize its characteristics. When a researcher applies the big data he/she must be anatomize the source and characteristics of the big data volume, verity, velocity, and veracity. It must be evaluated for continuity of storage, access, and scalability of the practiced domain from the very beginning to till date. It will take a look around that related to medical science. Traditional method has been followed up. Traditionally static paper, records are in the form of hard copy, files, and X-ray films [18,19] and related scripts, sample records, or pathological records [8], a patient’s detailed information are thus collected, and manipulation of large volume data has been carried out. The data is thus generated and collected at an immense speed in relation to medical science or to the bioinformatics [20e22]. The second characteristics of big data are its velocity, alluded to the rate at which data are produced and processed so that one can model a perfect database for medical science on

228 Chapter 8 medically observed data. Each day, data are generated at a very high speed with low cost for DNA sequencing [23,24]. At the same time, data are captured and shared at the same rate in which data are generated. Regarding public health, data analytics provide the facility to generate and share biomedical data by collaborating latest technical support, which helps in reducing time complexity and lowers the chances of erroneous data occuring. Big data helps in gathering real-time data which become the key to future reference. By gathering real-time data, it helps the researcher to find out the exact cause of infection in the shortest possible time. It may help in identifying the real cause of diseases, and at the right time proper treatment can be provided, which lowers the risk of more infection and reduces patient desolation and lethality. In the interim, real-time data are thus collected regarding medical issues like constant observation of particular disease, trauma monitoring for blood pressure, operating room that monitors for anesthesia, bedside heart monitors, etc., which can provide an analysis for making of a structure that can be the difference between life and death. There is the continuous flow of data through different data sources at a high velocity to generate a large volume of data. Data can generate periodically, and it can also generate through a batch system as well as near realtime or real-time batch processing at an immense speed. Therein lies velocity. Today, all the technologies are data-driven. The data today is not limited to only text, it has a variety of data for that it is so easy to develop and deal with any problems. Data can be of any format and any structure related to any domain. In the history of medical domain, the medical data only specific to text but now it varied from text to image, sound and also now follow different way to capture different types of data regarding work domain. The data is of a wide range of types and sources. The data are of a wide range of different types and sources which are gathered from different sources like image data of X-ray type, and data that are collected electronically relate to any physical issues or relating to this other structured data. The extensive variety of structured, unstructured, and semistructured data are of different dimensions which helps in making different methods from the medical point of view, and it becomes very challenging and interesting by applying big data analytics. The structured data are predefined, which means the medical issues relate to any patient, like cause of the disease, electronically stored health records, image files, X-rays, and sonography reports [25] that relate to health issues [18]. The structured data reside in fixed fields in the form of files or records. It will help in maintaining a routine check-up of any patient. A patient’s records and also the doctor’s and nurse’s records are kept in a structured manner so that a good schedule for doctor, patient, and nurse can be maintained. The structured data are so well formatted that can easily be accessed through maintained records by applying different big data analytics. When different techniques applying to medical data, it become easier to deal with the large voluminous data. It doesn’t require much effort for maintaining or manipulating data. When it maintained, the

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source of data can be of different types like database, flat file like patient health records or multidimensional health records [26]. Therein it mentioned as structured data. Unstructured data is not organized like structured data. In the medical system, data such as medical office records or any medical data stored in hard copies, i.e., prescription of doctor to patient or the handouts of any concerned nurse. Also, records like admission to hospital and discharge of particular patient, prescriptions to patient, radiographic films, CT scan, MRI, or other images [25]. The data collected from fitness devices, genetics and genomic observations, social media, and other media, and also from scientific observation, both structured and nonstructured data are cascaded. The structured and unstructured data are automatically converted according to the need of healthcare applications or according to the demand for latest technology. In the health care applications [27] the structured data are stored in EMRs and EHRs [28,29] including hospital name, address, treatment reimbursement codes, details of patient, and concerned physician to a particular section of a medical field. Accordingly, the unstructured data is converted to structured data according to need within the system. When all the system is carried out on pen and paper as it has been carried out from the very beginning, it is seen that the traditional system contains more erroneous data by keeping it manually, i.e., through pen and paper. There is a need to revise this traditional technique. Big data analytics is the solution and reduces errors through digitization of handwritten prescriptions. The potential of big data in health care lies in combining traditional data with new forms of data, both individually and on a population level [30]. We are already seeing data sets from a multitude of sources support faster and more reliable research and discovery. If, for example, pharmaceutical developers could integrate population clinical data sets with genomics data, this development could facilitate those developers gaining approvals on more and better drug therapies more quickly than in the past, and more importantly, expedite distribution to the right patients. The prospects for all areas of health care are infinite. Improving coordination of care, avoiding errors, and reducing costs depend on high-quality data, as do advances in drug safety and efficacy, diagnostic accuracy, and more precise targeting of disease processes by treatments. But increased variety and high velocity hinder the ability to cleanse data before analyzing it and making decisions, magnifying the issue of data “trust.” Veracity in health care data faces many of the same issues as in financial data, especially on the payer side: Is this the correct patient or hospital or payer or reimbursement code or dollar amount? Other veracity issues are unique to health caredAre diagnoses or treatments or prescriptions or procedures or outcomes captured correctly?da challenge [7] in recent trends of health care to data analytics.

230 Chapter 8

4. An architecture of large-scale platform to develop a predictive model It is difficult to develop most usurp iterative-computational algorithm for the purpose of designing well-grounded predictive model within the different Hadoop distributed file [31] system. Fig. 8.3 gives the idea about the flow chart of big data.

4.1 Types of big data 4.1.1 Primitive big data (a) Clinical operation i. Record clinical data. ii. Carry out the different operational techniques to patients. iii. Provide more clinically effective treatment. iv. Treatments are advanced techniques with cost effective. (b) Public health i. Analysis and treatments of diseases. ii. Find out the pattern of diseases for improving public surveillance and response to healthcare. clinical operation

public health primitive evidence based medicines partial profile analysis big data researchers and development

genomic analysis non - primitive

preadjudictionfraud analysis device or remote monitoring

Figure 8.3 Flow chart of big data.

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iii. Quicker development to vaccine. iv. Conversion of medical data to information. (c) Evidence-based medicines i. Analysis and collection of structured and unstructured data. ii. Doing prediction of risk of disease to patient. iii. Analyses varieties of clinical data. (d) Patient profile analytics: i. Apply segments and predictive analysis for finding individuals. ii. Changes the life style to a healthy life style by applying advanced analytics. iii. Helps in lowering the development of high-risk diseases. 4.1.2 Nonprimitive big data analytics (a) Researchers and Development i. It helps in developing a predictive model. ii. Use statistical tools and algorithm for advanced analytical techniques. iii. Analyses clinically trailed methods to medical data. (b) Genomic analysis i. Collect genomic data. ii. Analyses it to find the cause of diseases and appropriate treatment to it. iii. Helps in treatment of certain high-risk diseases. iv. Most important part of routine medical treatment. (c) Preadjudication fraud analysis i. Analysis claims against fraud to medical data. ii. Claims against authentication to medical data as well latest techniques. iii. It takes care of abuse and waste to patient history and genomic data. (d) Device or remote monitoring i. Collect and store real-time medical data. ii. Store fast-moving data from in-hospital and inehome devices.

4.2 Platform to big data 4.2.1 The Hadoop Distributed File System (HDFS) HDFS enables the underlying storage for the Hadoop [31] cluster. It divides the data into smaller parts and distributes it across the various servers/nodes. 4.2.2 Map Reduce Map Reduce provides the interface for the distribution of subtasks and the gathering of outputs. When tasks are executed, Map Reduce [31e34] tracks the processing of each server/node.

232 Chapter 8 4.2.3 PIG and PIGLatin Pig programming language is configured to assimilate all types of data (structured/ unstructured, etc.). It is comprised of two key modules: the language itself, called PigLatin, and the runtime version in which the PigLatin code is executed. 4.2.4 Jaql Jaql is a functional, declarative query language designed to process large data sets. To facilitate parallel processing, Jaql converts “‘high-level’ queries into ‘low-level’ queries” consisting of map-reduce tasks. 4.2.5 HBase HBase [31] is a column-oriented database management system that sits on top of HDFS. It uses a nonSQL approach. 4.2.6 Cassandra Cassandra [35] is also a distributed database system. It is designated as a top-level project modeled to handle big data distributed across many utility servers. It also provides reliable service with no particular point of failure and it is a No-Structure Query Language (NoSQL) system. 4.2.7 Avro Avro facilitates data serialization services. Versioning and version control are additional useful features. 4.2.8 Hive Hive is a runtime Hadoop support architecture that leverages Structure Query Language (SQL) with the Hadoop platform. It permits SQL programmers to develop Hive Query Language (HQL) [19] statements a kin to typical SQL statements. Fig. 8.4 gives the idea about different platforms of big data.

5. The model through big data analytics Dealing with functional networks [36] in identifying either continuous or categorical outcomes is required for a specific understanding of the problem in hand, data-driven architecture, intermediate communications, and data-flow mechanisms toward the desired target. The training processes use two types of learning: (1) Structural learning (2) Parametric learning

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HDFS Hive Hadoop architecture

PIG/PIGLatin Zo-Keper H-Base

big data plat forms

Jaql Casandra Oozie others Lucene Avro Mohout

Figure 8.4 Different big data platforms.

In structural learning [36], the initial topology is chosen based on some properties available to the designer, and the final architecture is simply modified using functional equation. In parametric learning [37], usually activation functions (neurons) are chosen by considering the combination of “basis” functions, and then are estimated using distinct optimization criteria, namely, least square, steepest-descent, conjugate gradient, and minimax methods. The implementations and learning processes of functional networks’ predictive modeling framework can be summarized as follows: (1) Statement of the problem and then specify the initial topology of the desire data-driven functional networks framework based on the domain-expert and of the problem in hand. (2) Simplify

5.1 An architecture of large-scale platform to develop a predictive model Develop a usurp iterative-computational algorithm for developing a well-grounded predictive model with the help of different Hadoop distributed file system (HDFS) [38]. Design of an in-memory computational model helps in reducing the I/O risk by developing the most reliable advanced analytic tools. There is some protocol that has been followed

234 Chapter 8 while developing the predictive model for the purpose of doing research related to a particular area of domain. Depending upon the type of data collected, it gives the idea of constructing predictive model. To this appropriate algorithm has been needed. In the Hadoop architecture, like HDFS, Map Reduce, these are the technique which can apply to design an in-memory computing solution for the large-scale functional network. 5.1.1 Map Reduce (Map and Reduce) Map Reduce is one of the major technique of HDFS system(s/s). It comprises of two primitive functions, i.e., map and reduce to analyze the processes of the distributed system. Map Reduce [33] is an open source software(s/w), i.e., it is scalable and faulttolerant to the particular HDFS file performance of the model. It works with the file-like hierarchical manner or load distributing method. There are several nodeworks with the file s/s. Key
value ðj2 ; U2 Þ Key
value ðj3 ; U3 Þ ðj2 ; U2 Þ ¼ ðt2 ; v2 Þ ðj3 ; U3 Þ ¼ ðt3 ; v3 Þ ðj1 ; U1 Þ ¼ ðt1 ; v1 Þ If one node fails to execute at any point of the program, then the other consecutive nodes of the same network or system are carried out with the program for the iterative execution without any interruption or delay, and it is considered as fault tolerant. The map-reduce technique mainly consists of two basic functions: (a) Map: It defines the set of keys and is responsible for the structured s/s. (b) Reduce: Helps in reducing the number of keys through a map function. It reduces the complete implementation by the multicore HPC implementation. Although computation of spark lowers the use of the hard disk, input output (I/O) and hence other technique should be mentioned. It is difficult to make an efficient and stable machine (m/c) earning algorithm by the application of spark technique. It contains one of the techniques among most reliable advanced analytics tools known as spark R and machine learning library (ML Lib). Spark R runs on top of spark and is an in-memory solution that claims 100 times the performance of Hadoop. It is a new technique of developing the framework for a model of handling big data. There is a different real-life application where functional networks become added in computational m/c learning for both categorical and continuous outcomes. A functional network is a framework having a wide range of uses. By the use of functional network, problems in probability, statistics, pattern recognition, functions approximation, science, bioinformatics, medicines, and structure engineering give the most reliable and efficient outcomes or result. Different

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functional network and training algorithms are present to give the efficient and most generalized standard o/p.

5.2 Functional network algorithm When developing algorithms, both learning and training algorithms have to be developed based on one of the learning techniques, i.e., either structure or parametric by the use of combination of independent functions: Independent function (b) ¼ {ba1, ba2, ba3

. . ..

ta ðxÞ ¼

na X

ban} to approximate the neuron function,

bai aix ; c a

i¼1

Coefficient of bai be the be the parametric of the functional n/w. 5.2.1 Functional network (n/w) can be learned by the use of one of the optimization techniques 5.2.1.1 Model selection It requires following certain common but important steps to build up a well-defined model to show up any objective to defined problem. The steps are: (1) Forward selection. (2) Background elimination. (3) Forward background elimination/exhaustive selection plays the vital role to select & validate a data model based on criterion. (4) Stopping criteria. A stopping threshold value is necessary while the model is executed on known statical quality measures for the predicted outcome. Then the accurate predictive model has been chosen according to the defined problems and then model will be ready to execute on realworld statistical measurement set for different symptoms of disease or varieties of a particular type of disease, e.g., cancer (leukemia, breast cancer, brain cancer, lymphoma, sarcoma, etc.). For regression/classification, it has been assured by the following data set: A ¼ fx; yg;

where X 3 zv Matrix size ¼ U  V v ¼ feature variable for input y 3 Z ðregression problem or continuous targetÞ Y 4 Z ðclassification problem or categorical targetÞ

In the both cases, i.e., in classification and prediction method, it has to determine the relationship o/p and input variables whether it may be linear or nonlinear.

236 Chapter 8 There are formulae which can help establish that the relationship between output and input variables Let the dataset; X ¼ fX1 ; X2 ...Xv g; v ¼ 1; 2; 3... Then, i. g(yi) ¼ f(Xi1,Xi2,..Xiv) and Q  i ii. g ; x ; .x ; Þ þ ˛ i; ¼ fðx i1 i2 iv ij where

Y ij

Y

ij

¼ Qðxi ε Ak jxi1 ; .xiv Þ 0;

for k ¼ 0; 1; ..C; c Y X j¼0

ij

¼ 1 and i ¼ 1; .n

Assume that g(.) is an invisible (one to one and onto) function, then above nonlinear models can be rewritten as: ¼ g
1 ½fðxi1; xi2; .xiv Þ þ ˛ i i. ¼ bðxi1; xi2; .xiv Þ þ ˛ i ii.

¼ g
1 ½fðxi1; xi2; .xiv Þ þ ˛ i ¼ bðxi1; xi2; .xiv Þ þ ˛ i

j ¼ 0; 1; 2; .:c;

Hence, the structures for both the functions are discovered.

6. Impact of big data Big data spurt most of the data that contained unstructured information which is not easy to analyze and develop a well-defined methodology for any domain specific to health care, or for any organization. It requires a method in computing architecture so that researchers or developers could be able to handle and process over data easily. The tools that need to gather knowledge and insights from the huge accretion of unstructured data are available through different sources like internet and other resources. There are number of companies and institutions that provide solution to generate, decipher, analyze, and visualize combined omics [19,39] and health clinical data. There are a number of companies that provide clinical data set which would be helpful for researcher as well as clinicians. Some of them are listed with the respective web browsing sites in Table 8.1 [16,39].

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Table 8.1: Different types of database repository and websites. Company name

Web search

Nextbio DNAnexus Genome International Corporation GNS Health care Pathfinder Context Matters Appistry Beijing Genome Institute CLC Bio Pathway Genomics Foundation Medicine Knome 23andme Counsyl Personalis

http://www.nextbio.com http://www.dnanexus.com http://www.genome.com http://www.gnshealthcare.com http://www.pathfindersoftware.com http://www.contextmattersinc.com http://www.appistry.com http://www.genomics.cn/en http://www.clcbio.com http://www.pathway.com http://www.foundationmedicine.com http://www.knome.com http://www.23andme.com http://www.counsyl.com http://www.personalis.com

6.1 Examples to complex biomedical information There are some companies who provides solutions to various health care issues. 6.1.1 Dell health care solutions The Dell Health Care company provides the proper techniques and technology with which the researchers do their research to monitor the total health care system and try to rebuilt a information-driven health care system which will accelerate the innovations toward research in new area or subarea of medical data science. The respective company give a predictive model on a number of innovations in life science with medical records stored electronically. Example: http://www.dell.com/Learn/us/en/70/healthcaresolutions?c¼us&l¼en&s¼hea. 6.1.2 IBM health care and life sciences The IBM Health Care and Life Science Company provide the latest analytical techniques and solutions related to the different health care issues and enable the particular health organization to achieve greater efficiency within different operations of health issues of patient by merging structured and unstructured data. Collaborating with the information gathered, the IBM health care company provide more improved outcomes and merge with new partners for a more sustainable, personalized, and patient-centric system. Example: http://www-935.ibm.com/industries/healthcare.

238 Chapter 8 6.1.3 Intel health care Intel health care helps in making the framework for IT tools for health issues. The respective company also provides the services to the government sector, various health care organizations, and different technology innovators worldwide. The company combines the various data regarding health issues and provide best solutions to different issues. Example: http://aws.amazon.com. 6.1.4 Amazon web services It provides necessary computing techniques to reserve solution to the different health care organizations. The company thus creates the environment that includes CPUs, storage place like RAM, ROM, networking, and operating s/s for the sake of hardware infrastructure. This provides the necessary service in the field of biomedical and scientific research area, and also in scientific field in case of biomedical fields. Example: http://aws.amazon.com. 6.1.5 GE health care life science Provides expertise and tools for a wide range of applications, including the basic research of cells and proteins, drug discovery research, as well as tools to support large-scale manufacturing of biopharmaceuticals. Example: http://www3.gehealthcare.com/en/Global_Gateway. 6.1.6 Oracle life sciences Delivers key functionalities built for pharmaceutical, biotechnology, clinical, and medical device enterprises. Oracle maximizes the chances of discovering and bringing to market products that will help in treating specific diseases. Example: http://www.oracle.com/us/industries/life-sciences/overview/index.html. 6.1.7 Cisco health care solutions Cisco health care solutions offers different types of solution for the life sciences, including specific hardware and cloud computing for reliable and highly secure health data communication and sharing across the health care community. Example: http://www.cisco.com/web/strategy/healthcare/index.html.

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6.2 Personalized medicines Day to day, huge amount of omics data [39,40] needs proper treatment as storing, analyzing, visualization of data, and rearrangement of stored data [17,27,41]. Big data provide its advanced analytic techniques to fulfill the needs of data to resolve medical issues. Omics data has been used to produce “short” reports on stored data for researcher and clinicians for their further research-oriented work. Short reports are valuable in terms of finding new solutions or treatment methods to health care problems. The new genomic industry required the transformation techniques to deal with huge omics data [40] for solving conventional health care issues. Somehow cloud computing is the cause of the evolution of the genomic industry and providing the solution that has been transplanted in the medicines and life sciences worlds. There is also another solution to deal with genomic data [27] that changes the path that has been adopted for the transformation and utilization of genomic data. Graphics processing units (GPUs) are the technique to big data which improves in computational power as well and brings improvement in conventional approaches to medical science (Table 8.2).

7. Ethical issues Through the study for meta-analysis [42] of medical, five major ethical themes emerged from the literature: i. informed consent, ii. privacy, iii. ownership, Table 8.2: Different companies for personalized medicines and omic solutions. Company

Websites

Personalis

http://www.personalis.com

Foundation Pathway Genomics

http://www. foundationmedicine.com http://www.pathway.com

Knome

http://www.knome.com

23andme

http://www.23andme.com

Counsyl

http://www.counsyl.com

Services It is agenomic-scale diagnostic servive center provides accurate genetic sequence data. It is amolecular information company provide routine clinical care to cancer patient. Address a variety of medical issues, incorporates customized and scientifically validation of data. Analyze data through software based test for gene network as well genomes. Provides educational tool and information to each and every individuals to learn and for their research on DNA analysis. Provide the test for genome mutation and validation.

240 Chapter 8 iv. epistemology, and v. the “big data divide.” The ethical issues come into view according to interpretation and designation of themes of health care that carried out through data analytics. Ethical issues [43,44] to the health care system transpire according to frequency but it merely highlights. The outcome to be presented is focused on the researchers’ analysis and interpretion of the problem domain and different issues that arise during the research or related to any specific issues. The outcomes of applying analytical techniques on the subject of medical issues [45] have been discussed and chooses one of the four given reasons: i. ii. iii. iv.

draw attention to common interpretations of ethical themes and concepts; to emphasize individual cases and issues that reveal unique ethical aspects of big data; highlight studies with an in-depth analysis of ethical concepts and issues; identify the gaps in the discussion in need of further research.

7.1 Ethical themes 7.1.1 Consent Consent is defined as permission for something to happen or agreement to do something serious. 7.1.1.1 Informed Consent

Permission granted is full knowledge of the possible consequences typically that which is given by a patient to a doctor for treatment with knowledge of the possible risks and benefits. Most of the literature survey addresses granting the consent to the researcher as the primary objective. History reveals, it is the considerations to the single study participation except sharing, aggregating, or even repurposing data. Such type of consent is problematic because of the data point connectivity that is described by big data analytics [5], which indicate or give the sense of both what the efficacy of greater uncertainty which are normal and what the data reveal at the time of consent for the future research work. Let us take an example of secondary effects of pharmaceutical company. It reveals the basic objective of pharmaceuticals is medical treatment and hence not only takes into consideration data from multiple clinical trials but also takes into consideration data from informal sources such as data gathered, via social network, self-reporting and search engine queries. Hence by establishing a link for multiple data sets through connecting different data sets, research has been carried out but the outcomes have not been revealed accurately. Thus, data subjects cannot be told or, in other terms consent cannot be informed irrespective of future use and consequence of their data, which are baffling at the time of collecting and aggregating data (Table 8.3).

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Table 8.3: Ethical themes and the number of sources associated with it. Theme

No. of sources

Informed consent Privacy Anonymization Data protection Ownership Epistemology Proper/Control (Big data divide) Digital Divide (Big data divide)

34 44 20 14 12 14 22 22

There is a big difference between historical and big data. Problems arise when there is a difference present in between historical data and recent data while granting consent for a special purpose. Historical data while establishing connectivity between the data point need granting of consent. But it is a key feature to big data for identifying the novel connection between the data points that is desired to be exposed and creativity. It is free of difficulties and hence allows exposing, sharing, and aggregating the global type of consent through the journey of data covering both political and electronic border of any institution or organization. It covers all the data through informed and noninformed consent of medical observation. Because sometimes it is quite unpredictable while finding the right solution to medical issues. But explicitly single secondary instance consent has not be used for secondary use, rather it has the explicit consent to use it. However, it is difficult even conducting a trial after trial to get such consent. It is difficult to get solution to medical instances; therefore, tensions arise between potential benefits to big data and need for consent. To this, technical assistance is not the justified solution. It is little bit difficult for anonymizing the data that are quite sufficient to fill up the need for consent. Somehow technical support is needed to help eradicate or be able to tackle such problems for which solution is “big data” also that create a temptation or researcher to think beyond the limitation to the use of data which creates the initial consent. Research, the work done against any research area again and again sought with the initial consent, is not research instead of involving the scientist or researcher on duty to participate with the temptation to use huge data [46]. For some extent, the initial consent arise from the problem while crossing the boundary of using huge data for biomedical or any other research through the different analytical techniques of big data. Somehow, “consent” cannot be informed to the researcher concerned to particular a domain area. Because many of the observation leads to both formal and nonformal way of collections of data and the concerned data subject is unaware of data collected as well as its source and future use of data. Recognizing this, there are number of mechanisms to consent:

242 Chapter 8 7.1.1.2 Single-instance consent

Big data has been designed in such a manner that it reveals all the concerned information between the data points. It is not precise to any particular point of discussion. Singleinstance consent is a barrier to researchers for using big data. Initially, consent cannot be expressed or informed to any initially collected huge data, which is the exception to big data analytics. Hence, it is well said that single-instance consent is based on belief. 7.1.1.3 “Broad” and “Blanket” consent mechanisms

Many of the places, “Broad” [30] and “Blanket” [47,48] consent has been used as a replacement for single-instance consent, which preauthorize as the future secondary analysis. The corresponding consent mechanism has been recognized to shorten the autonomy of collected data subjecs,t which is forbidden by big data analytics [45]. It is one of the broad types of consent that covers all future activities related to different research. For example, in biobanks [49,50], it does not allow to give prior knowledge or information about the data that has been aggregated. 7.1.1.4 Tired consent

Tired consent has been used to put the permit upon specific uses of data subject. It allows the researcher to use data subject but with certain limitations. For example, only once the data has been used for one form of research it cannot be used in another research. For example, data that is used in cancer research cannot be used in dermatological research. 7.1.2 Data protection Data need privatization. It needs well-defined protection protocols that everyone should strictly follow while using the data subject for their research. The United States and the European Union have immensely strict data protection laws. But somehow it would require tight protocol, because sometimes, it may not able to protect all medically relevant or health-related data that is driven by big data analytics. While using huge data that are subjected or concerned to big data, it must be of open access source. It does not bear privacy for the data. Hence, it leads to a result of governing ethical systems for sensitive health data and the values that govern the database or any ethical review board of any institution or organization named as custodians [51] to data or database. Real problems arise while using or collecting data from social media databases or information stored related to bioissues. As the possibility that many of the databases are patient-drives databases, such patient-driven databases mostly possess less draconian requirements while comparing with biobank or repositories to clinical-trailed data. Biobanks and clinical data repositories pass safety regulations enforced by very stringent restrictions enforced by governance bodies, which is not normally found out in case of patient-driven databases.

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7.1.3 Privacy While dealing with big data, the use of data subjects to medical domain, privacy is a matter of concern. Most of the databases follow as well as adopt strict privacy protocols. Hence for that by avoiding anonymity, confidentiality can also be well maintained. In other point of view, privacy should be well maintained so that there is no arise of anonymity. Many of the reviewed literature have shown the concerns about privacy to data and also made the policy in such a way that it should maintain concepts to alternatives as freedom of information or autonomy. A line has been established between autonomy of information and confidentiality. Some of privacy policy in big data analysis has been discussed as “invasiveness.” In big data analysis, it is very important when combining data from different data sources that are internet based (social media, different data repair unities) or geological locations through invasiveness [52]. In social media, even if they have some privacy policy, still they unaware of the extent to which the data can be scraped. According to Nissenbaum’s context-sensitive approach [53,54], it is discussed how data subjects are being scraped from various data sources and how it has been analyzed outside of “highly contest-sensitive space” and how it has violated the expectation to privacy of the data subject. It is necessary to build up realistic privacy norms in the context of big data. There are a number of piracy issues that exist related to data subject while storing, collecting the data, retrieval of data subject. Data grows exponentially, however traditionally, it is stored and collected and also has been bounded by human perception and cognition. Through techniques like autonomous and automated collection of information deviated from traditional technique data grows in a very immense speed which become more personal and very highly detailed data. It is termed as “age of big data.” Other issues like fragility potential, perpetuity obsolescence of s/w, and presence of hackers are of major privacy concerning subjects to data. While discussing, the primary factor regarding different issues to big data for medical point of use, it is context-sensitive. For expanding the durability with life span of data, it should be taken care of while collecting medical data and its storage system. For this it should increase or raise the issue regarding privacy policy. 7.1.4 Ownership Ownership is a critical issue to conceptualization and it is a quite complex notion. Ownership mostly discuss about the authorization of owner to data. In a simple way it gives the idea about “the intellectual property.” The data subject, used for medical domain has required proper modification i.e., removal of erroneous data, redundant data, and storing them properly in a well managed and synchronized database. Sometimes data stored are raw data and sometimes have erroneous data. There is another side of

244 Chapter 8 ownership to data maintaining, data indignity, but ownership is strictly only for accessing and modifying the data to database but while accessing, ownership is still allowed only to researcher, analyst for doing any research work, innovation, and development of intellectual property. Every database has followed its own restriction of accessing through ownership. If one goes for type of the ownership then it can be divided into two categories, i.e., (1) Control data; (2) Rights to “benefits from.” Ownership should possess the right to pass on the control over data. When taken for research work it must be used by trained data analysts or clinicians because some extent of raw data is harmful for research-oriented use or for treatment while observing the history of patient. Control and empowering of the data subject through techniques means tracking and checking the uses of data and also its existence in database and manipulation through different analytical techniques, so that it will be helpful in preventing the erroneous and secret data existence and enable certain strict protocol to database storage and usage. When any possibilities exist to identify and re-identify the required data and analysis if any over the existing data, then at the time only data have the control over the uses of data [10]. Big data analytics have followed some techniques which comprises of data custodians to allow control over the data subject. All the data are digitally stored, hence the big data techniques offered follow some strict protocol to have control over accessing data. For which the data subject must be well-maintained and used by the clinician. Such type of methods to deal with data will be beneficiary to both individuals and communities where the data has been produced. To some extent, it is quite risky to provide data without any restriction for the purpose of accessing and using the data. Using raw data, it is practically harmful and useless when diagnosing any major disease or for treatments. 7.1.5 Epistemology Today’s data thus collected and observed are more fascinating because of instrumentation over intelligent machines. The algorithms have been used to analyze different techniques, which may exceed human comprehensiond“the intelligent citizen cannot read the programs that run our data set” [12,55]. In other words, “the natural world and its human observers are being ever instrumented with intelligent machines . as people we are, in Olga Kuchinsheya’s memorable phrase, becoming our own data” [56]. Big data analytic techniques overcome the different difficulties arising during interpretation of radiographies and complexities while predicting patient diseases through patient history. It has become more accurate and significant due to its large in size, greater opacity. The big data analytic

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technique is the solution to the complexities like rationale of the algorithm, complicated mathematical equation or reasoning, complexity to vast database while analyzing this. It is quite difficult of questioning about the validation of relationship and findings, for the researcher and experts as well general public. It becomes critical investigations of big data, comparable to questioning the outputs like “black box” [57]. 7.1.6 Objectivity Need is the cause of invention. When data has been collected, are thus in the form of both structured and unstructured. The role of interpretive frameworks has been discount by the idea exist behind the object to data-led. It makes the sense to data and thus data collected from the environment to deal with or to handle with need of researcher and clinician. While dealing with medical databases for running problem, if any complexity arises that can be handled by big data analytic techniques as “objective” without any exegesis of human.due to the increasing abstract and the complex practices, so many questions arise and big data analyzing technique is the answer to those questions. It is the objective of developing such an advanced analytical method as big data analytics, which would have all the answer to the questions. Many data analysts may discuss the data collected from real world or physical world data, interpretive data of human behavior, or their social realities. Also an argument has been made against and in favor of the differences between “objective” and use of raw data that specifies human characteristics. The objective of big data is nevertheless taken into consideration for such type of real-world existing data to known behavior. Regardless, the point of discussion for the issue is that the objective of big data in medical domain which mainly narrates the biomedical data based on social behavior or biological observations. Very often such biomedical data represents inherently natural data and are capable of describing complex phenomena like data-driven science without having contextual knowledge, sense, or interpretation. Increase in abstract and more complex practices which fine-up the big data. It will be helpful in extraction, transformation, data management, collection, cleansing and analyzing, indexing of existing data and as well as in visualizing human behavioral data. In unstructured data, it searches the patterns, eliminates the noise and redundant data if any, and facilitates the data set boundaries. In turn objective has been fulfilled on the purpose and further investigation on data is being carried out. To rely on mere big data ignores the different quality and types of data set. For example, data of the patient’s biological history, medical treatment, pathological observation, any other medical data or any clinical job. The aggregation of data on observations is to find out the basic link ups, which becomes prone to selection, confounding, and measurement of biases. If data taken into consideration for processing without any presence of human knowledge, interpretation or understanding, then the variable quality of data undermines the justification of the work carried out on behalf of physical bodies.

246 Chapter 8 There are other ethical issues related to big data present in current and foreseeable biomedical contexts and issues. One of the issues is anonymization is nothing but closely linked up with each other on the virtue of ethical issues. Privacy can be practiced through big data by removing and identifying information while anonymization was frequently required and minimum requirement to protect privacy of data subject through aggregation of data. It is easy to identify the data, like observational data, health records, or even small pieces of reidentified genomic data, etc. Data concerned with medical research is somehow anonymized through data subjects. For bio-banking research, the study of data thus analyzed and hence populated the research in the respective domain through large data set stored.

8. Conclusion Reviewing literature is basic to have a primary knowledge about any field. Medical data science is a vast domain and a buzzword today. While people going to have any treatment face lots of problems due to adopting traditional techniques. Big data [58] is a new ray to clinical treatment as it adopts so many techniques by which the clinical data can stored very quickly as well as synchronized in well managed database which leads to “deep” conceptual analysis. Also, it comes up with a great success to research in biomedical [37] and pathological department. Big data helps in storing, retrieving, and analyzing omic data [19] and combined with clinical information stored about the particular patient, it leads to easy interpretation of exact data and gives proper treatment to the particular patient with the emerging technology.

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248 Chapter 8 [31] R.C. Taylor, An overview of the Hadoop/MapReduce/HBase framework and its current applications in bioinformatics, BMC Bioinformatics 11 (12) (2010) S1. [32] A.V. Nguyen, R. Wynden, Y. Sun, HBase, MapReduce, and integrated data visualization for processing clinical signal data, in: AAAI Spring Symposium: Computational Physiology, vol. 2011, 2011. [33] J. Dean, S. Ghemawat, MapReduce: simplified data processing on large clusters, Communications of the ACM 51 (1) (2008) 107e113. [34] M.C. Schatz, CloudBurst: highly sensitive read mapping with MapReduce, Bioinformatics 25 (11) (2009) 1363e1369. [35] http://en.wikipedia.org/wiki/Apache_Cassandra. [36] L. Liu, X. Bai, H. Zhang, J. Zhou, W. Tang, Describing and learning of related parts based on latent structural model in big data, Neurocomputing 173 (2016) 355e363. [37] O.Y. Al-Jarrah, P.D. Yoo, S. Muhaidat, G.K. Karagiannidis, K. Taha, Efficient machine learning for big data: a review, Big Data Research 2 (3) (2015) 87e93. [38] A. Oussous, F.Z. Benjelloun, A.A. Lahcen, S. Belfkih, Big Data technologies: a survey, Journal of King Saud University-Computer and Information Sciences 30 (4) (2018) 431e448. [39] F.F. Costa, Big data in biomedicine, Drug Discovery Today 19 (4) (2014) 433e440. [40] P. Kovatch, A. Costa, Z. Giles, E. Fluder, H.M. Cho, S. Mazurkova, Big omics data experience, in: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, ACM, 2015, p. 39. [41] E. Elsebakhi, F. Lee, E. Schendel, A. Haque, N. Kathireason, T. Pathare, R. Al-Ali, Large-scale machine learning based on functional networks for biomedical big data with high performance computing platforms, Journal of Computational Science 11 (2015) 69e81. [42] R. DerSimonian, N. Laird, Meta-analysis in clinical trials revisited, Contemporary Clinical Trials 45 (2015) 139e145. [43] G. Booch, The human and ethical aspects of big data, IEEE Software 31 (1) (2014) 20e22. [44] B.D. Mittelstadt, L. Floridi, The ethics of big data: current and foreseeable issues in biomedical contexts, Science and Engineering Ethics 22 (2) (2016) 303e341. [45] T.B. Murdoch, A.S. Detsky, The inevitable application of big data to health care, JAMA 309 (13) (2013) 1351e1352. [46] J.P. Ioannidis, Informed consent, big data, and the oxymoron of research that is not research, The American Journal of Bioethics 13 (4) (2013) 40e42. [47] J.E. Lunshof, R. Chadwick, D.B. Vorhaus, G.M. Church, From genetic privacy to open consent, Nature Reviews Genetics 9 (5) (2008) 406. [48] J.A.K. Kegley, Challenges to informed consent: new developments in biomedical research and healthcare may mark the end of the traditional concept of informed consent, EMBO Reports 5 (9) (2004) 832e836. [49] L. Jonsson, U. Landegren, Storing and using bio banks for research, in: The Use of Human Bio Banks. Ethical, Social, Economical and Legal Aspects, Universitetstryckeriet, Uppsala, 2001, p. 4. [50] B.M. Knoppers, M. Saginur, Bio-banking, The Cambridge Textbook of Bioethics (2008) 166e173. [51] H. Liyanage, S. De Lusignan, S.T. Liaw, C. Kuziemsky, F. Mold, P. Krause, S. Jones, Big data usage patterns in the health care domain: a use case driven approach applied to the assessment of vaccination benefits and risks, Yearbook of Medical Informatics 23 (01) (2014) 27e35. [52] A. Markowetz, K. Błaszkiewicz, C. Montag, C. Switala, T.E. Schlaepfer, Psycho-informatics: big data shaping modern psychometrics, Medical Hypotheses 82 (4) (2014) 405e411. [53] P. Ohm, Sensitive information, Southern California Law Review 88 (2014) 1125. [54] R.H. Sloan, R. Warner, Developing foundations for accountability systems: informational norms and context-sensitive judgments, in: Proceedings of the 2010 Workshop on Governance of Technology, Information and Policies, ACM, 2010, pp. 21e26. [55] M. Altaf-Ul-Amin, F.M. Afendi, S.K. Kiboi, S. Kanaya, Systems biology in the context of big data and networks, BioMed Research International 2014 (2014).

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[56] G.C. Bowker, Data Flakes: An Afterword to ‘‘Raw Data’’is an Oxymoron. Raw Data’’is an Oxymoron, MIT Press, Cambridge, 2014. http://www.ics.uci.edu/*vid/Readings/bowker_data_flakes.pdf. [57] D.T. Yu, D.L. Seger, K.E. Lasser, A.S. Karson, J.M. Fiskio, A.C. Seger, D.W. Bates, Impact of implementing alerts about medication black-box warnings in electronic health records, Pharmacoepidemiology and Drug Safety 20 (2) (2011) 192e202. [58] G. Manogaran, C. Thota, D. Lopez, V. Vijayakumar, K.M. Abbas, R. Sundarsekar, Big data knowledge system in healthcare, in: Internet of Things and Big Data Technologies for Next Generation Healthcare, Springer, Cham, 2017, pp. 133e157.

Further reading O. Gottesman, H. Kuivaniemi, G. Tromp, W.A. Faucett, R. Li, T.A. Manolio, M. Brilliant, The electronic medical records and genomics (eMERGE) network: past, present, and future, Genetics in Medicine 15 (10) (2013) 761. T. Margoni, The roles of material transfer agreements in genetics databases and bio-banks, in: Comparative Issues in the Governance of Research Biobanks, Springer, Berlin, Heidelberg, 2013, pp. 231e249. O. Morozova, M.A. Marra, Applications of next-generation sequencing technologies in functional genomics, Genomics 92 (5) (2008) 255e264. W. Raghupathi, V. Raghupathi, An overview of health analytics, Journal of Health & Medical Informatics 4 (132) (2013) 2.

CHAPTER 9

Semisupervised fuzzy clustering methods for X-ray image segmentation Tran Manh Tuan1, Tran Thi Ngan1, Do Nang Toan2, Cu Nguyen Giap3, Le Hoang Son2 1

Faculty of Computer Science and Engineering, Thuyloi University, Hanoi, Vietnam; 2VNU Information Technology Institute, Vietnam National University, Hanoi, Vietnam; 3ThuongMai University, Hanoi, Vietnam

Chapter Outline 1. Introduction 252 Part 1: Theory background 1.1 1.2 1.3 1.4

253

Image segmentation problem 253 Data clustering 255 Fuzzy clustering 255 Semisupervised fuzzy clustering 256 1.4.1 Semisupervised entropy regularized fuzzy clustering-eSFCM

256

Part 2: The combination of eSFCM and OTSU in image segmentation

258

2.1 The general diagram of the integration between the eSFCM and OTSU 258 2.2 OTSU threshold algorithm in image processing 258

Part 3: Semisupervised fuzzy clustering with spatial feature

261

3.1 The general framework 261 3.2 Determining suitable additional information 262 3.3 The semisupervised fuzzy clustering algorithm (SSFC-SC) 263 3.3.1 Dental image segmentation model 263 3.3.2 Solving the segmentation problem using Lagrange multiplier 265 3.4 Fuzzy satisficing method and semisupervised clustering method in segmentation problem (SSFC-FS) 267 3.5 The properties and consequences from solution analysis 271 3.6 The advantages of the proposed algorithms 275

Part 4: Defining the suitable additional information for SSFC-FS algorithm 275 4.1 4.2 4.3 4.4

The framework of the SSFC-FSAI method 275 The set of additional information functions 276 Defining an appropriate additional information 278 Advantages of the new algorithm 280

Part 5: The results of implementations and applications

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252 Chapter 9 5.1 Dental X-ray image dataset 282 5.1.1 Data description 282 5.1.2 Defining features 282 5.1.3 The validity indices and evaluation criteria 283 5.2 The performance among segmentation methods 283 5.2.1 Experiments in dental X-ray image dataset 283 5.2.2 The results of clustering algorithms according to changing of parameters

283

2. Conclusions 286 Acknowledgments 288 References 288

1. Introduction The X-ray image segmentation problem is an important stage in information systems that support dentists to diagnose teeth-related diseases such as cracked and hidden teeth, cavities, missing teeth, and periodontitis [1,2]. For the best results, the information of images including space and feature information is used. In recent research, certain methods were used to solve this problem. Based on the structure, color, and texture of dental images, there are threshold methods [3,4] and clustering methods [5]. In these methods, the features of space are not mentioned. Traditional clustering methods include two main groups: supervised and unsupervised clustering. Apart from these, semisupervised clustering, the combination of supervised and unsupervised clustering, is another trend. This model uses some information provided by users in clustering progress (additional information). In a semisupervised clustering model, one of the most important issues in semi-supervised clustering is to identify suitable additional information that can yield high accuracy for a given problem, e,g. image segmentation. Original clustering methods can be considered in two types: hard clustering and soft clustering, which includes fuzzy clustering methods [6]. Hard clustering methods assign each data sample to a unique cluster. Otherwise, in soft clustering methods, particulary fuzzy clustering methods, each data point can be assigned into different clusters with different probabilities. Thus, each data sample is featured by a matrix that shows the inclusion levels to different clusters. Inheritance of these attributes, semisupervised fuzzy clustering is an integration of fuzzy clustering and useful additional information [7]. In our previous research, a measurement of H-max distance on an intuitionistic fuzzy set [8] was proposed. Apart from that, the authors analyzed performances of intuitionistic t-norms and performances of intuitionistic t-conorms. They also extracted some good properties obtained by combination between the classification of these fuzzy t-norms and

Semisupervised fuzzy clustering methods for X-ray image segmentation 253 t-conorms and new measurement. The measurement was applied to medical diagnosis using a real data set. Especially on medical diagnosis using dental X-ray images, different methods such as a new H-max distance measurement by Ngan et al. [9], a novel fuzzy clustering algorithm by Ali et al. [10], and the integration of segmentation technique and soft computing by Son et al. [11] were proposed. Semisupervised fuzzy clustering is basis for solving dental diagnosis problem due to better results of X-ray image segmentation process. At the beginning, the original images are segmented, and the regions with high probability of disease are indicated. The diagnosis decisions will be made based on these regions. The main contents of this chapter include five parts: Part 1: Theory background This section presents some of concepts related to image segmentation, dental X-ray images, and clustering methods. Part 2: The hybrid clustering algorithm This part introduces a semisupervised fuzzy clustering algorithm that is the combination of FCM, OTSU, and eSFCM to solve the segmentation problem. Part 3: The semisupervised fuzzy clustering algorithm with spatial feature This part presents a clustering method using spatial information of image for dental X-ray image segmentation problem. A new approach to solve the optimal problem is also presented. Part 4: The best suitable additional information for SSFC-FS In this part, different ways to define additional information are presented. After that, the most appropriate additional information is figured out. Part 5: Experiments and applications This final section gives the various experimental results of mentioned methods on a real dataset. This chapter summarizes the results of the authors’ researches from 2015 to 2019, and most of them have been published in international journals.

Part 1: Theory background 1.1 Image segmentation problem Image segmentation is one of the most important steps in image processing and analysis [12,13]. However, this step is also the most difficult step in image processing. In practical dentistry, X-ray image segmentation is an indispensable process to support dentists effectively in making diagnosis of dental diseases such as stomatitis, periodontal disease, and tooth inflammation [1,2]. Image segmentation is a necessary step for the following

254 Chapter 9 (A)

(B)

Figure 9.1 Some examples of radiography: (A) X-ray dental image; (B) X-ray with missing teeth.

processes, including diagnosis support [14], to determine isolated teeth or other parts in dental X-ray images [15]. Fig. 9.1 shows two examples of X-ray images. There are three parts in dental X-ray image including teeth area, dental structural area, and background area [16]. These parts are classified using the values of grayscale with high, medium, and low level, respectively. The analysis process of dental X-ray image is more complicate than common images [17]. There are many things that affect to the X-ray image quality such as noise, low contrast or the mistakes in the scan process. These things lead to the decrease of segmentation performance. Moreover, the missing hole in X-ray images (Fig. 9.1B) cannot be proceeded by common techniques [18]. Thus, the data mining methods are proposed to get the higher accuracy in X-ray image segmentation [19]. The segmentation methods are developed from different techniques of image processing methods [20] based on pixels, boundaries, and regions. They can be categorized into two main groups: using pure image processing techniques [21,22] and using clustering methods [23]. The first group includes threshold methods (OTSU [2]), boundary and region methods. The second group uses popular methods such as K-means [20], Fuzzy C-means (FCM) [6], etc. The methods in the first group have to transform images into binary images by using a threshold a complex curve to define boundaries. The big challenge of the methods in the first group is defining the threshold or common boundaries of dental X-ray images [4]. Otherwise, the methods in the second group do not need to know the information of threshold or curve. However, determining parameters and boundaries among clusters is the difficulties of clustering methods [22,24e26].

Semisupervised fuzzy clustering methods for X-ray image segmentation 255

1.2 Data clustering Data clustering [16] is a process that groups a set of data points that have the similarity to each other, in which objects of one cluster have high similarity and objects belong to different clusters are not similar. Data clustering is used as a data-mining technique to search and discover the clusters of potential data samples in a big dataset. Based on the obtained results, the useful information is supplied to the decision-making process. As mentioned, clustering methods have two types: hard clustering and fuzzy clustering. Herein, the fuzzy clustering methods are the main concern, and they are presented in the following section.

1.3 Fuzzy clustering FCM [6] is based on membership grade ukj of data element Xk in jth cluster. The objective function was defined as: J¼

N X C X k¼1 j¼1

2 um kj kXk  Vj k /min

(9.1)

in which m is the fuzzifier, C is number of clusters, N is number of data points. We have the kth data point Xk ˛ Rr, data set X ˛ RN, the membership grade of Xk in jth cluster ukj, the centroid of jth cluster Vj. The constraints of objective function in (9.1) are C X j¼1

ukj ¼ 1;

ukj ˛ ½0; 1;

ck ¼ 1; N

(9.2)

The Lagrange multiple method will results in the solutions of problems (9.1) and (9.2) as the centroid of clusters in (9.3) and the membership grade in (9.4): C P

Vj ¼

um kj Xk

k¼1 C P

k¼1

ukj ¼

(9.3) um kj

1

C P i¼1

!1 m1 kXk  Vj k kXk  Vi k

(9.4)

The pseudo code, input, and output of FCM are: Input: N elements of dataset X ˛ Rn; C; fuzzifer (m); threshold (ε); and MaxIteration

256 Chapter 9 Output: U (Membership) and V (Cluster centers) Begin: p¼0 ðtÞ Generate ukj randomly satisfying (9.2) Repeat p¼pþ1  ðtÞ  Calculate Vj ; j ¼ 1; C using Eq. (9.3) ¼ 1.C, k ¼ 1.N) as in (9.4) Calculate   ðpÞ ukj (j ðp1Þ    ε or p > MaxIteration Until U  U

1.4 Semisupervised fuzzy clustering It is developed by combining fuzzy clustering with additional information provided by users that help to lead, monitor, and control clustering progress. The additional information can be classified into three kinds [27]: must-link and cannotlink constraints, class labels, and a predefined membership. Each model will choose one of the information to solve a typical problem. The membership degree is often used in image segmentation problems. For example, Zhang [27] applied entropy principle to reduce number of dimensions and proposed a new approach by combining entropy component into objective function. Apart from that, Yasunori [28] introduced a semi-supervised fuzzy clustering method that derives from FCM and uses new membership function in clustering progress. Bouchachia va` Pedrycz [24] used additional information to define values of membership matrix ukj via ueik .

1.4.1 Semisupervised entropy regularized fuzzy clustering-eSFCM Semisupervised fuzzy clustering based on entropy regularization was first introduced by Yasunori et al. [28] in 2009. In 2012, Yin [29] justified the entropy coefficients and improved eSFCM to get better clustering results. C X j¼1

ukj  1;

ukj ˛ ½0; 1;

ck ¼ 1; N

(9.5)

The original cluster centers are set as: N P

u2kj Xk

V j ¼ k¼1N P

k¼1

u2kj

; j ¼ 1; .; C

(9.6)

Semisupervised fuzzy clustering methods for X-ray image segmentation 257 To calculate by Mahalanobis distance, the covariance matrix of samples is defined: C X N   T 1 X A¼ u2kj xk  V j xk  V j N j¼1 k¼1

(9.7)

The distances are computed by (in which A ¼ P1) dA2 ðx1 ; x2 Þ ¼ ðx1  x2 ÞT Aðx1  x2 Þ

(9.8)

eSFCM has objective function [30,31] as: JðU; VÞ ¼

N X C X k¼1 j¼1

ukj kXk  Vj k2A

þl

1

N X C  X    ukj  ukj lnukj  ukj  /min

(9.9)

k¼1 j¼1

With constraints in (9.2) and objective function (9.9), we obtain: ! 2 C X elkXk Vj kA 1 uki ; k ¼ 1; N; j ¼ 1; C ukj ¼ ukj þ C P lkX V k2 i¼1 k i A e

(9.10)

i¼1

where

kXk  Vj k2A

¼ dAðk; jÞ and cluster centers is N P

Vj ¼

ukj Xk

k¼1 N P

k¼1

;

j ¼ 1; C

(9.11)

ukj

With the input including X (a dataset), C (number of clusters); matrix U, threshold ε and maxIteration, the steps of eSFCM are given below. - Generate vj and compute matrix p based on U with P¼

C X N   T 1 X u2kj xk  vj xk  vj N j¼1 k¼1

- Set p as 1 - Loop: p ¼ p þ 1; Calculate ukj (j ¼ 1.C, k ¼ 1.N) using elkXk Vj kA ukj ¼ ukj þ C P lkX V k2 k i A e 2

i¼1

1

C X i¼1

! uki

258 Chapter 9 ðtþ1Þ

Define Vj

by N P

Vj ¼

ukj Xk

k¼1 N P

k¼1

;

j ¼ 1; C

ukj

  The algorithm stops when U ðpÞ  U ðp1Þ   ε or p > maxIteration

Part 2: The combination of eSFCM and OTSU in image segmentation 2.1 The general diagram of the integration between the eSFCM and OTSU The hybrid model eSFCM-OTSU [32] is presented in a general diagram as in Fig. 9.2 [32] below. The input parameters of about framework include orthodontic X-ray image, number of cluster, values of fuzzifier, a threshold, and a stop value. In the hybrid model, the background area needs to be removed from the radiography (if it exists) using OTSU method. Then, FCM method is applied to the main area of image. Lastly, eSFCM is used to improve the results of clustering method in postprocessing of segmentation progress. The model has some advantages inherited from both the expert system and medical informatics. The most suitable set of parameters of this model can be used by other scientists in order to apply in similar researches.

2.2 OTSU threshold algorithm in image processing The OTSU algorithm was introduced in Ref. [2] and used in Ref. [33] by Rad, Rahim, and Norouzi. The input image is separated into three parts based on density including the background or soft area, the bone area, and the teeth area. But, in many instances, the density of teeth near the bone area can be classified as background region and image (or main) region. This classification can be used when applying OTSU method. OTSU is one of the most famous thresholding methods in pixel-based image processing. We can define a suitable threshold in many different ways. To partition an image using a

Semisupervised fuzzy clustering methods for X-ray image segmentation 259

Figure 9.2 The eSFCMeOTSU framework.

global threshold T is the easiest technique. A pixel is labeled as the main region (r0) and background region (r1) depending on its gray level (f(x)). The formula of this can be presented as in (9.12)  ro if f ðxÞ  T . (9.12) gðxÞ ¼ r1 if f ðxÞ < T The output of applying OTSU method here is a binary image. This kind of image makes the image analysis process simpler. When an image must be clustered into more than two groups, more than one threshold were used to determine various clusters. The thresholds T1, T2, ., Tn are equidistant

260 Chapter 9

Figure 9.3 Input image.

points, Ti with the values from min to max, with i ¼ 1, 2, ., n. For each pixel, the value f(x) is calculated by averaging such pixel’s R, G, B values. We can define that: 8 cluster 1 if f ðxÞ  T1 > > > > > if T1 < f ðxÞ  T2 > < cluster 2 A pixel is in ....... . (9.13) > > > cluster n if Tn1 < f ðxÞ  Tn > > > : cluster n þ 1 if f ðxÞ > Tn Then, in membership matrix U, the elements uij is set to 1 if pixel j is in cluster i. Otherwise, uij is set to 0. Example 1. For an image size 9  9 as in Fig. 9.3, the result of using OTSU method is shown in Fig. 9.4. In which, initial threshold T (0) ¼ 3 after five iterations

Figure 9.4 Binary image obtained by using OTSU method.

Semisupervised fuzzy clustering methods for X-ray image segmentation 261

Part 3: Semisupervised fuzzy clustering with spatial feature 3.1 The general framework Fig. 9.5 [34] illustrates the diagram of the SSFC-SC algorithm [34]. This algorithm has Xray input images and necessary parameters. The dental features are extracted from the input image in order to make a dental feature database. The additional information of image is defined. FCM classifies the input images into clusters. The number of clusters is the same with that in the SSFC-SC algorithm. Membership matrix obtained from FCM together with spatial information are used in SSFC-SC. This stage shows the formulas of cluster centroids and new membership matrix. To evaluate the SSFC-SC performance, the validity indices of the implemented are computed and compared with other related methods. This model has some advantages. At first, the model creates a database of features of dental X-ray images. It also represents the problem of dental image segmentation as a semi-supervised learning clustering problem. Using Lagrange multiplier method, the optimal problem set by this model was solved in order to get the optimal solutions. Besides, the additional information was determined in this model. There are five fundamental features extracted [35] from a radiography by various methods. These features include entropy, edge value and density, local binary patterns (LBP), Begin

A dental X-ray image and parameters, Expert knowledge

Dental feature extracon

Use FCM to get prior membership matrix

Determine addional informaon

Design a new semi-supervised fuzzy clustering model and SSFC-SC algorithm

A segmented image

Evaluate results by validity indices End

Figure 9.5 The general framework of SSFC-SC.

262 Chapter 9 red-green-blue (RGB), gradient feature, and patch level. Results from the extraction will be stored in a feature database.

3.2 Determining suitable additional information The way used to define the additional information for SSFC-SC method relates to the dental feature database follows these steps: - Step 1: The minimum value of membership degree for each data point and other establishes are defined. - Step 2: Denote pw1, pw2, pw3, pw4 to calculate the values of dental features, respectively. Then, the normalization of these features is applied to get the feature of each pixel. wi ¼

pwi . max fpwi g

(9.14)

- Step 3: Define the features: l P

u2 ¼

i¼1

( max

wi

l P i¼1

).

(9.15)

wi

- Step 4: Synthesize the membership values of three above steps to achieve the additional information.  au1 ; when u1  u2 ukj ¼ ; (9.16) au2 ; when u1 < u2 where a ˛ ½0; 1 is the expert knowledge, used to support for the additional information determining process in SSFC-SC. Example 2. Suppose that we have the optimal membership matrix of FCM in Table 9.1 in which Ci stands for Cluster i, i ¼ 1, ., 5 and Pj stands for Point j, j ¼ 1, ., 3. Then, a list of values u1 is computed (Table 9.2).

Table 9.1: The final membership matrix of FCM.

P1 P2 P3

C1

C2

C3

C4

0.22 0.5 0.15

0.12 0.05 0.3

0.13 0.15 0.1

0.48 0.15 0.2

C5 0.05 0.15 0.25

Semisupervised fuzzy clustering methods for X-ray image segmentation 263 Table 9.2: The values of u1. C1 e e e

P1 P2 P3

C2

C3

e 0.05 e

e e 0.1

C4 e e e

C5 0.05 e

The dental features and the list of values of u2 are shown in Tables 9.3 and 9.4 respectively.

Table 9.3: The dental features.

P1 P2 P3

EEG

LBP

RGB

0.5 0.3 0.6

0.6 0.3 0.4

0.6 0.6 0.8

Gradient 0.7 0.1 0.7

Sum 2.4 1.3 2.5

Table 9.4: The values of u2.

u2

P1

P2

P3

0.96

0.52

1.0

With a ¼ 0:7, the additional membership matrix is given in Table 9.5.

Table 9.5: The additional membership matrix. C1 P1 P2 P3

0 0 0

C2 0 0.364 0

C3 0 0 0.7

C4 0 0 0

C5 0.672 0 0

3.3 The semisupervised fuzzy clustering algorithm (SSFC-SC) We present the modeling process to define the objective function of dental X-ray image segmentation problem in Subsection 3.3.1. Then, the obtained optimal problem is solved by Lagrange multiplier method.

3.3.1 Dental image segmentation model Based on above analysis, objective function of dental X-ray image segmentation is set: J ¼ J1 þ J2 þ J3 /min.

(9.17)

In Eq. (9.17), the objective function includes J1 the traditional objective function of FCM, J2 represents the spatial features of dental images and J3 the additional information.

264 Chapter 9 J1 ¼

N X C X k¼1 j¼1

2 um kj kXk  Vj k .

(9.18)

In which, ukj presents membership degree of data point Xk in cluster j, and m is the fuzzifier parameter, Vj is the center of jth cluster. The role of j2 bases on the idea of using a comparative space window. From the observation of two neighbor pixels with the similar values of pixels. If the difference between two values is greater than a threshold, they surely belong to different clusters. Let N1 be an optimal size of a comparative window, a function SIik is defined from the comparative space window as in Eq. (9.19). N1 P

SIik ¼

j¼1

1 uij dkj

N1 P j¼1

;

(9.19)

1 dkj

where uij is the value of membership function of kth pixel in jth cluster, dkj is the Euclidean distance between two points (xk, yk) and (xj, yj). A fuzzy distance is given as in Eq. (9.20).   (9.20) Rik ¼ kxk  vi k2 1  aeSIik ; where a ˛ ½0; 1 is monitoring parameter. When a ¼ 0, the function in (9.20) is the traditional Euclide distance. The objective function J2 needs to be minimized by fuzzy distance among pixels in a cluster with high similarity. Thus, the first part of J2 is defined as in (9.21). J2a ¼

N X C X k¼1 j¼1

2 um kj Rjk .

(9.21)

In Step 2 of Section 3.2, we determine i dental features (i ¼ 1, ., 4). Indeed, the second part of J2 is used to maximize the similar features. From that, this part of J2 (J2b) is set as: ! N X C l X X m 1 (9.22) ukj wik . J2b ¼ l i¼1 k¼1 i¼1 It means that: J2 ¼ J2a þ J2b .

(9.23)

Semisupervised fuzzy clustering methods for X-ray image segmentation 265 The objective function J3 presents the additional information part supporting for clustering process with the formula: J3 ¼

N X C X k¼1 j¼1

jukj  ukj jm kXk  Vj k2 ;

(9.24)

where ukj are additional information values. Finally, the objective function of optimal problem used to solve dental image segmentation problem is: ! N X C N X C N X C l X X X X 1 2 2 um um um wik J¼ kj kXk  Vj k þ kj Rjk þ kj l i¼1 k¼1 j¼1 k¼1 j¼1 k¼1 i¼1 (9.25) N X C X m 2 jukj  ukj j kXk  Vj k þ k¼1 j¼1

Satisfying the constraints: C X j¼1

ukj ¼ 1;

ukj ˛ ½0; 1;

ck ¼ 1; N.

(9.26)

3.3.2 Solving the segmentation problem using Lagrange multiplier To solve problem in (9.25e9.26), we use the Lagrange multiplier by the following steps: Take the derivation of (9.25), we get N N X X vJ m ¼ 2 ukj ðXk  Vj Þ  2 um kj jukj  ukj jðXk  Vj Þ. vVj k¼1 k¼1

(9.27)

Set this derivation as 0, the centers of clusters are obtained: N  P

 þ ju  u j xk um kj kj kj

Vj ¼ k¼1  . N  P þ ju  u j um kj kj kj k¼1

(9.28)

266 Chapter 9 With the Lagrange function LðUÞ ¼ J þ

N X k¼1

0 lk @

C X j¼1

1 ukj  1A

(9.29)

With m ¼ 2, we have the formula of membership grade: lk þ 2ukj kXk  Vj k2

ukj ¼ 2

l 1P 2kXk  Vj k2 þ R2jk þ wik l i¼1

From the constraints in (9.26), we have 0 BX B C 0lK ¼B B @ j¼1

ukj kXk  Vj k2 2kXk  Vj k2 þ R2jk þ

0 BX B C B B @ j¼1

l 1P wik l i¼1

(9.30)

1 C, C !  1C C A

1 2 2kXk  Vj k2 þ R2jk þ

!.

l 1P wik l i¼1

1

(9.31)

C C !C C A

Combine (9.30) and (9.31), we determine the membership matrix. The obtained solutions from this method are center of clusters in (9.28) and membership matrix in (9.30), (9.31). The detail of SSFC-SC algorithm is presented as: -

Dental features are extracted. The optimal result of FCM on each input (UFCM) is taken. The addition information is defined. Let p ¼ 0 and generate the membership matrices randomly. Loop: p ¼ p þ 1; ðtÞ Calculate Vj (j ¼ 1; C) using Eq. (9.28); ðtÞ

(9.31); Calculate ukj (k ¼ 1; N; j ¼  1; C) using (9.30),  The algorithm stops when U ðpÞ  U ðp1Þ   ε or p > maxIteration.

Semisupervised fuzzy clustering methods for X-ray image segmentation 267

3.4 Fuzzy satisficing method and semisupervised clustering method in segmentation problem (SSFC-FS [35]) Based on the contents described in above section, the multiple objective optimal problem is presented in (9.25e9.26), and a novel algorithm solving such problem is introduced. This algorithm enhances the accuracy of above dental image segmentation methods to get the highest performance. Proposed model uses dental X-ray image features as an objective function, and an interactive fuzzy satisficing method is used to solve multi-objective problems and get optimal solutions. The theoretical results show the better qualities of extracting solutions compared with those of Lagrange method. Problem analysis In Eq. (9.32), the objective function have the form as J ¼ J1 þ J2 þ J3 /min.

(9.32)

With the separate parts is defined clearly in formulas (9.18), (9.23), (9.24) Applying the Weierstrass theorem, the existence of optimal solutions of this problem can be stated in Lemma 1. Lemma 1. In the multiple objective optimal problem (9.25), (9.26), the objective function is continuous on an unempty compact set. Thus, the problem has continuous and bounded global optimal solutions. From the result of Lemma 1, the formulas of optimal solutions of mentioned problem are: Define the optimal solutions of multiple objective optimal problem: Initialization: Solving the subproblems using Lagrange multiplier method: - The first sub-problem: J1(u) / min, u ˛ RC  N satisfying conditions (9.26). In this problem, the formulas of centers of clusters and membership degrees are: N P

um jk Xk

Vj ¼ k¼1N P

u1jk ¼

lk m  djk

!

k¼1

;

1 m1

;

(9.33)

um jk

lk ¼ 0 @

1 C P j¼1

1 m  djk

!

1 m1

1m1 ; A

(9.34)

268 Chapter 9 Where dkj ¼ kXk  Vj k2 , j ¼ 1.C; k ¼ 1.N. Then, the objective function J1 can be rewritten as: J1 ¼

N X C X k¼1 j¼1

um jk djk .

(9.35)

- The second subproblem: J2 (u) / min, u ˛ RC  N with the constraints (9.26). l P Set ajk ¼ R2jk þ 1l wki , j ¼ 1.C; k ¼ 1.N, we have: i¼1

J2 ¼

N X C X k¼1 j¼1

um jk ajk .

(9.36)

The optimal solutions of this problem are shown in following equation: !1 m1 bk 1 2 ; bk ¼ 0 ujk ¼ ! 1 1m1 . m  ajk m1 C P 1 @ A j¼1 m  ajk

(9.37)

- The third subproblem: J3 (u) / min, u ˛ RC  N with conditions in (9.26). It’s easy to find out the centers of clusters: N P

jujk  ujk jm Xk

Vj ¼ k¼1N P

k¼1

.

(9.38)

m

jujk  ujk j

With the notation djk is the same as in Problem 1, the objective function J3 is rewritten by: J3 ¼

N X C X k¼1 j¼1

jujk  ujk jm djk .

The optimal solutions of this problem are u3jk and its formulas have this form: 0 1m1 !1 B C m1 B C 1  ujk g k 3 B C þ ujk ; gk ¼ B C . ujk ¼ C m  djk P 1 @ A 1 j¼1 ðm  djk Þm1 From the solutions of subproblem, the pay-off table is established as in Table 9.6:

(9.39)

(9.40)

Semisupervised fuzzy clustering methods for X-ray image segmentation 269 Table 9.6: Table of solutions from subproblems. Objective functions Optimal solutions ð1Þ ujk ð2Þ ujk ð3Þ ujk

J1

J2

J3

z11

z12

z13

z21

z22

z23

z31

z32

z33

Let: z1 ¼ minfzt1 ; t ¼ 1; 2; 3g; z1 ¼ maxfzt1 ; t ¼ 1; 2; 3g; z2 ¼ minfzt2 ; t ¼ 1; 2; 3g; z2 ¼ maxfzt2 ; t ¼ 1; 2; 3g; z3 ¼ minfzt3 ; t ¼ 1; 2; 3g; z3 ¼ maxfzt3 ; t ¼ 1; 2; 3g; 1 2 3

ðrÞ Sp ¼ u ; u ; u ; r ¼ 1; ai ¼ zi .

(9.41) (9.42) (9.43) (9.44)

Iterations: Set r [ 1 Step 1: The fuzzy satisficing solutions of sub-problem is defined by following equations: m1 ðJ1 Þ ¼

J3  z3 J1  z1 J2  z2 ; m2 ðJ2 Þ ¼ ; m3 ðJ3 Þ ¼ . z1  z1 z2  z2 z3  z3

(9.45)

Based on these functions, the combined satisficing function is obtained: Y ¼ b1 m1 ðJ1 Þ þ b2 m2 ðJ2 Þ þ b3 m3 ðJ3 Þ/min;

(9.46)

With the conditions of parameters: b1 þ b2 þ b3 ¼ 1 and 0  b1 ; b2 ; b3  1

(9.47)

Then, the optimal solutions of problem with objective function in (9.46) and the constraints in (9.26), (9.48) (with additional conditions in (9.48) below) are defined as below. ðrÞ

Ji ðxÞ  ai ; i ¼ 1; 2; 3: Combining with (9.45), Eq. (9.46) can be rewritten as  b3 z3 b1 z1 b2 z2 b1 b2 b3 Y¼ J1 þ J2 þ J3  þ þ . z1  z1 z2  z2 z3  z3 z1  z1 z2  z2 z3  z3

(9.48)

(9.49)

Taking the derivation of Y in (9.49) by ujk we get vY b1 vJ1 b2 vJ2 b3 vJ3 ¼ þ þ þ hk ; j ¼ 1.C; k ¼ 1.N vujk z1  z1 vujk z2  z2 vujk z3  z3 vujk

(9.50)

270 Chapter 9 For each set of parameters (b1, b2, b3) satisfying the conditions in (9.47), we get the   ðrÞ optimal solutions of original problem, denoted as uðrÞ ¼ ujk CN

Step 2: - If mmin ¼ minfmi ðJi Þ; i ¼ 1; .; 3giq, with q is a threshold provided by users then u(r) is not acceptable solutions. Otherwise, we check if u(r);Sp, we add u(r) into Sp. - If needing to expand Sp, we increase r by r ¼ r þ 1 and test the conditions: If r > L1 or after L2 continuous iterations, the set Sp do not have any new solutions then ðrÞ ðrÞ we set ai ¼ zi ; i ¼ 1; 2; 3 and take any index h from {1, 2, 3} to set ah ˛ ½zh ; zh Þ and repeat step 1. - If no needing to expand Sp go to the step 3. Step 3: - Remove the abnormal solutions on Sp. - The algorithm ends. The formulas of the solutions are stated in Lemma 2 below. Lemma 2. For each set of given parameters (b1, b2, b3), the solutions u(r) of optimal problem in (9.49) satisfy the following constraints: vY b1 vJ1 b2 vJ2 b3 vJ3 ¼ þ þ þ hk ¼ 0; j ¼ 1.C; k ¼ 1.N vujk z1  z1 vujk z2  z2 vujk z3  z3 vujk ðrÞ

ðrÞ

5ujk ¼

(9.51)

ðrÞ

b3 h  djk  ujk  k z3  z3 2 ! ; j ¼ 1.C; k ¼ 1.N ðrÞ ðrÞ ðrÞ b3 b1 b2 djk þ þ  ajk z1  z1 z3  z3 z2  z2 ðrÞ

C P j¼1 ðrÞ

hk ¼ 2 

b3  djk  ujk z3  z3 ! 1 ðrÞ ðrÞ ðrÞ b3 b1 b2 þ  ajk djk þ z1  z1 z3  z3 z2  z2

C P j¼1

ðrÞ

ðrÞ

1 !

ðrÞ

b b1 b djk þ 2  ajk þ 3 z1  z1 z3  z3 z2  z2

(9.52)

; k ¼ 1; N;

Semisupervised fuzzy clustering methods for X-ray image segmentation 271 ! ðrÞ ðrÞ  2 2 b3  ðrÞ b1 ðrÞ Xk  ujk þ u  ujk z3  z3 jk k¼1 z1  z1 ! . ¼ ðrÞ ðrÞ  2 2 N P b3  ðrÞ b1 ðrÞ  ujk þ u  ujk z3  z3 jk k¼1 z1  z1 N P

ðrÞ

Vj

(9.53)

3.5 The properties and consequences from solution analysis In previous sections, for a given problem, the optimal solutions u(r) is found by fuzzy satisficing method. Herein, we present some analysis of theoretical results of these achieved solutions such as convergence rate, parameters limitations, and the comparison with other solutions of different approaches. All the proofs of following theorems, lemmas, and consequences are omitted. The detail of these can be seen in our publications. ðrÞ

At first, by using the formula of Vj in Eq. (9.53), it’s easy to show that the solutions have following properties and propositions. Property 1. If b2 ¼ 1, b1 ¼ b3 ¼ 0, the centers of clusters are undefined. Property 2. Solutions u(r) are continuous and limited by (b1, b2, b3). Proposition 1. For all values of parameters (b1, b2, b3), from Eq. (9.52) we have: ! # " ðrÞ ðrÞ ðrÞ ðrÞ ðrÞ b3 b3 hk b1 b2 djk þ  djk  ujk  þ  ajk  z3  z3 z1  z1 z3  z3 z2  z2 2 ðrÞ

b  3  djk  ujk ; k ¼ 1; N; j ¼ 1; C; z3  z3

(9.54)

Second, the optimal solutions of fuzzy satisficing method are compared with those of local Lagrange multiplier method by considering the optimization problem in Eqs. (9.25)e(9.26). The solutions of Lagrange method is a local optimal ones. Thus, these following propositions are proved. Proposition 2. The optimal solutions obtained when solving the problem (9.55)e(9.57) are defined as: N  P

 þ ju  u j XK um kj kj kj

Vj ¼ k¼1N   ; P m ukj þ jukj  ukj j k¼1

9.55)

272 Chapter 9 lk þ 2ukj kXk  Vj k2

ukj ¼ 2

2

2kXk  Vj k

þ R2jk

0 BX B C l k ¼B B @ j¼1

ukj kXk  Vj k2 2kXk  Vj k2 þ R2jk þ

0 BX B C B B @ j¼1

l 1P þ wik l i¼1

l 1P wik l i¼1

(9.56)

1 C, C !  1C C A

1 2 2kXk  Vj k2 þ R2jk þ

!;

l 1P wik l i¼1

1

(9.57)

C C !C C. A

To evaluate the solutions of fuzzy satisficing method (FS) and those of Lagrange method (LA), we use IFV index, where a higher value of IFV means a better quality. The IFV index is calculated by: ( " #2 ) C N N X 1 X 1 X 1 SDmax IFV ¼  u2kj log2 C  log2 ukj ; (9.58) C j¼1 N k¼1 N k¼1 sD SDmax ¼ maxkVk  Vj k2 ; k6¼j ! C N 1 X 1 X sD ¼ djk C j¼1 N k¼1

(9.59) (9.60)

Denote IFV(1) and IFV(2) are the values of IFV index from two mentioned methods respectively. From Eq. (9.58) we have: 8 9 > 2 32 > > > > lk !2 lk > > > > > C N N < d u  d u  X jk jk 7 = SDmax 1 X 1 X jk jk 2 6 1 ð1Þ 2 6log2 C  7 log ; IFV ¼  2 2djk þ ajk 5 > C j¼1 > N k¼1 2djk þ ajk 4 N k¼1 sD > > > > > > > > : ; (9.61)

Semisupervised fuzzy clustering methods for X-ray image segmentation 273

IFVð2Þ

8 9 > 2 32 > > > > > hk hk > > !2 > < = C > N N w d u  w d u  X X X 3 jk jk 3 jk jk 6 7 1 1 1 2 2 6 7 ¼ C  log log 2 2 >N k¼1 ðw1 þ w3 Þdjk þ w2 ajk 4 C j¼1 > N k¼1 ðw1 þ w3 Þdjk þ w2 ajk 5 > > > > > > > > : ; 

SDmax . sD (9.62)

The difference of these values can be stated with the assumption shown in Lemma 3: Lemma 3. In the Lagrange multiplier method, the value of parameter lk is defined using (9.57). Then, the comparison of the solutions using Lagrange method with those of FS method can be stated if the values of (b1, b2, b3) satisfy some certain conditions lk ! lk ! N djk ujk  X 2  log C  1 2  log2 2 N k¼1 2djk þ ajk 2djk þ ajk

djk ujk 

b3 h 1 djk ujk  k z  z 2 3 3 A @ b1 b3 b2 djk þ þ ajk z1  z1 z3  z3 z2  z2 0

(9.63)

b3 h 1 djk ujk  k N X z  z 2 1 3 3 A;  @log2 C  log2  b1 b3 b2 N k¼1 þ ajk djk þ z1  z1 z3  z3 z2  z2 0

ajk ¼ R2jk þ

l 1X wik : For all k ¼ 1; N; j ¼ 1; C. l i¼1

(9.64)

Theorem 1. With the values of given (b1, b2, b3) satisfy the conditions in Lemma 3, we get: 8 IFVð1Þ  IFVð2Þ ¼

1 1 SDmax   C N sD

> 2 32 > > l k !2 lk > > C X N < djk ujk  N djk ujk  7 X X 2 6 27 6log2 C  1 log2 4 > 2djk þ ajk N k¼1 2djk þ ajk 5 j¼1 k¼1 > > > > :

9 2 32 > > hk hk > !2 > N = w3 djk ujk  w3 djk ujk  X 6 7 1 2 2 6 7 0  C  log log 2 2 N k¼1 ðw1 þ w3 Þdjk þ w2 ajk 4 ðw1 þ w3 Þdjk þ w2 ajk 5 > > > > ; (9.65)

274 Chapter 9 From proof of Theorem 1, a property of the solutions get from FS is shown as below. Property 3. The optimal solutions achieved by applying Interactive FS are better than by using Lagrange multiplier. Thirdly, the value of IFV at the rth iteration has upper and lower bounds shown as in following theorems. Theorem 2. The lower bound of IFV values on optimal solutions u ¼ u(r) in FS is given by: IFVð2Þ 

1 SDmax   ½log2 C2 . C2 sD

(9.66)

To determine the upper bound, we introduce the definition of limitation L as: b3 h 9 djk ujk  k = z3  z3 2 . log2  L ¼ lim ; b1 b3 b2 ujk /0: k¼1 þ ajk djk þ z1  z1 z3  z3 z2  z2 8 N

N < log 2 a ¼ Bj < a1 ¼ 2Bj 1 X 5 ; B ¼ log C  Ajk ; j ¼ 1; .C j 2 1 1 > > N k¼1 : log 2 a ¼ Bj  : a2 ¼ 2Bj  ln 2 ln 2

(9.91)

Find the second order derivation of IFV by a: 8 !  C N 2 X X v IFV 1 SDmax 1 < 1 2 2 ¼   ujk ðBj  log2 aÞ Bj  log2 a  C N: ln 2 sD va2 j¼1 k¼1 þ 2a 

C N X X j¼1

k¼1

! u2jk

 1 1   Bj  log2 a  a ln 2 ln 2

(9.92)

9 = 1 þ ðBj  log2 aÞ   a ln 2 ; 

From that, we have: v2 IFV ða1 Þ > 0; va2

v2 IFV ða2 Þ < 0 va2

(9.93)

280 Chapter 9 This means that when a ¼ a2 , the value of IFV gets the maximum. This value gets the minimum if a ¼ a1 . Thus, the value of a2 is: a2 ¼ 2Bj ln 2 1

(9.94)

Step 4: Choose this membership matrix and its parameter values as the additional function. Example 1. Given an image size 3  3:

The weight matrix of this image is:

Apply FCM to segment this image, the centers of clusters and membership degrees are shown as in following tables:

Centers of clusters

Membership matrix

Using IFV criterion to select the value of a that makes IFV maximal. Table 9.7 shows that the fourth function (hyperbolic sine function) is the most suitable for the dental image.

4.4 Advantages of the new algorithm There are some advantages of SSFC-FSAI such as: - SSFC-FSAI is better than SSFC-FS in term of clustering quality since each dental image is processed with a different additional function that is best fit with the image, and hence increasing the overall accuracy.

Table 9.7: The values of IFV to get the most suitable additional function. ith Function

IFV

1

1.7456

2

3

4

10.212

1.6093

133.49

5

1.6137

6

1.8087

7

2.4646

8

1.3421

9

0.4113

10

1.3421

Ui 0.25 0.22 0.30 0.11 0.11 0.68 0.25 0.25 0.25 0.47 0.53 0.00 0.25 0.25 0.25 0.25 0.25 0.26 0.30 0.22 0.16 0.00 0.00 0.76 0.00 0.00 0.31 0.00 0.00 0.76

0.35 0.21 0.23 0.98 0.01 0.01 0.24 0.26 0.26 0.00 0.99 0.01 0.23 0.27 0.27 0.22 0.28 0.27 0.44 0.09 0.01 0.99 0.00 0.00 0.45 0.00 0.00 0.99 0.00 0.00

0.35 0.22 0.21 0.98 0.01 0.01 0.24 0.26 0.26 0.00 0.92 0.08 0.23 0.27 0.27 0.23 0.27 0.28 0.30 0.30 0.08 0.99 0.00 0.00 0.31 0.00 0.00 0.99 0.00 0.00

0.23 0.26 0.29 0.01 0.03 0.95 0.26 0.26 0.24 1.00 0.00 0.00 0.26 0.26 0.24 0.24 0.23 0.30 0.04 0.41 0.27 0.00 0.00 0.96 0.00 0.00 0.41 0.00 0.00 0.96

0.21 0.28 0.30 0.04 0.22 0.70 0.25 0.25 0.25 1.00 0.00 0.00 0.25 0.25 0.25 0.25 0.25 0.26 0.06 0.31 0.31 0.00 0.00 0.73 0.00 0.00 0.31 0.00 0.00 0.73

0.25 0.28 0.24 0.12 0.57 0.18 0.25 0.25 0.25 0.98 0.00 0.02 0.25 0.25 0.25 0.25 0.25 0.25 0.02 0.42 0.14 0.00 0.66 0.00 0.00 0.42 0.00 0.00 0.66 0.00

0.25 0.28 0.23 0.14 0.56 0.16 0.25 0.25 0.25 0.80 0.00 0.20 0.25 0.25 0.25 0.26 0.24 0.25 0.27 0.05 0.34 0.00 0.66 0.00 0.00 0.34 0.00 0.00 0.66 0.00

0.20 0.32 0.28 0.03 0.79 0.14 0.25 0.25 0.25 1.00 0.00 0.00 0.25 0.25 0.25 0.06 0.45 0.42 0.36 0.05 0.29 0.00 0.82 0.00 0.00 0.36 0.00 0.00 0.82 0.00

0.21 0.32 0.26 0.02 0.91 0.06 0.25 0.25 0.25 1.00 0.00 0.00 0.25 0.25 0.25 0.26 0.24 0.27 0.11 0.06 0.41 0.00 0.93 0.00 0.00 0.42 0.00 0.00 0.93 0.00

282 Chapter 9 - SSFC-FSAI automatically determines the values of parameters for the highest quality of the clustering algorithm. - The new parts cooperate with the old ones in an unified framework that supports effectively for medical diagnosis.

Part 5: The results of implementations and applications 5.1 Dental X-ray image dataset 5.1.1 Data description The dataset used in our implementations consists of 66 dental X-ray images. These images are taken by VATECH machine of patients whose ages are from 18 to 38, at Hanoi Medical University Hospital, Vietnam in 2014, 2015. The X-ray images are divided into five groups: -

Group of fractured stump: 10 patients. Group of hidden teeth: 13 patients. Group of cavities: 10 patients. Group of missing teeth: 10 patients. Group of resorption bone around teeth: 13 patients.

In detail, the statistics of patients is shown as in Table 9.8. The dental X-ray images in our dataset are the detail radiography of the patients’ teeth. Based on these images, dentists can check if there is any problem of dentistry or teeth health in general. From these dental X-ray images, we extract five common features.

5.1.2 Defining features The average of the features of all pixels in that image is considered as the features of whole image. By extracting basic features, we create a feature database of 66 images. The statistics of these features of every image in dataset are shown in Table 9.9. Table 9.8: The information of patients. Sex of patients Disease groups Fractured stump Hidden teeth Cavities Missing teeth Resorption bone Total

Male 6 8 5 6 8 33

Female 6 7 7 6 7 33

Age of patients 16e22 0 3 5 5 2 15

23e30 4 6 2 4 6 22

31e38 8 6 5 3 7 29

Semisupervised fuzzy clustering methods for X-ray image segmentation 283 Table 9.9: The statistics of features in whole dataset. Feature EEI-M LBP-M RGB-M Gradient-M Patch-M

Mean 44.02 153.31 117.48 0.42 0.026

Derivation

Max value

Max value

Median

3.25 2.29 9.19 0.0192 0.004

51.12 157.07 139.03 0.45 0.033

31.58 147.25 82.94 0.33 0.19

43.97 153.84 117.83 0.42 0.26

5.1.3 The validity indices and evaluation criteria Goal: Validity indices are used to assess the accuracy of image segmentation methods in dental X-ray images. The most suitable values of parameters for such problem are figured out by practice to generate the best validity indices. In our experiments, following validity indices are used: DB, SSWC, PBM, IFV, BH, CH, BR, DL [37].

5.2 The performance among segmentation methods From the dental X-ray image dataset, the algorithms presented in previous sections (eSFCM-OTSU, SSFC-SC, SSFC-FS, SSFC-FSAI) and several related methods (FCM [6], OTSU [2], eSFCM [28]) are installed on a PC using Matlab code. To evaluate these algorithm, the mentioned validity indices are calculated as well, and its result is described in the next section.

5.2.1 Experiments in dental X-ray image dataset To get a overview of 66 images instead of the observation on each separate image, the means and variances of the values in each validity indices obtained by using various algorithms are computed and presented as in Table 9.10.

5.2.2 The results of clustering algorithms according to changing of parameters The purpose of this stage is to find out most appropriate parameters for each input image to get the highest performance of algorithm SSFC-SC. The parameters include the number of clusters that is set as C ¼ 3, and the value of a, that is 0.9 in previous section. To see the changes of SSFC-SC algorithms by the change of these parameters, the experimental results on this dataset with various values of a are presented as below. In order to know how different membership matrices in additional information would affect the clustering quality of SSFC-FSAI, we validate this method on an image and by different (manual) membership matrices.

Table 9.10: The means and variances of validity indices of different algorithms. Algorithm PBM DB IFV SSWC CH BH BR DL

FCM 34,592.6 0.54Eþ08 0.661 0.006 31.34 245.41 0.629 0.008 8,773,911 0.67Eþ14 1476.96 40,315.4 1.9Eþ07 1.64Eþ14 5.09Eþ09 8.51Eþ18

OTSU 39,448.83 857,679,906 0.846 1.034 Inf 0.656 0.01 6,422,162 1.68Eþ14 838.30 90,125.07 1.5Eþ07 6.85Eþ14 2.43Eþ09 4.18Eþ18

The bold value is the best value of each validity index.

eSFCM 30,337.89 5.69Eþ08 0.708 0.01 448.25 77,655.09 0.646 0.01 8,657,363 1.69Eþ14 1520.20 50,465.31 1.9Eþ07 1.51Eþ14 5.34Eþ09 7.66Eþ18

eSFCM-OTSU 8873.58 1.27Eþ08 0.655 0.0005 1943.32 2312.72 0.574 0.052 7,657,634 1.68Eþ14 1156.83 49,833.73 1.7Eþ07 1.62Eþ14 4.67Eþ09 5.67Eþ18

SSFC-SC

SSFC-FS

SSFC-FSAI

51,309.25 1.43Eþ09 0.804 0.037 47.07 430.12 1.067 5.43 10,649,217 1.98Eþ14 1673.08 107,667.7 ¡2.5Eþ07 1.24Eþ14 5.89Eþ09 8.08Eþ18

49,323.87 2.34Eþ09 0.833 0.045 52.87 562.73 1.263 4.36 11,535,244 0.83Eþ14 2109.98 178,232.9 2.3Eþ07 0.98Eþ14 6.78Eþ09 6.08Eþ18

54,509.24 0.98Eþ09 0.824 0.044 88.78 372.2 0.983 3.23 10,662,842 1.32Eþ14 2327.37 186,227.7 2.4Eþ07 1.18Eþ14 5.87Eþ09 8.00Eþ18

Semisupervised fuzzy clustering methods for X-ray image segmentation 285 Table 9.11: Results of validity indices obtained by using SSFC-SC with C [ 3 and different values of a. PBM SSWC DB IFV CH BH BR DL PBM SSWC DB IFV CH BH BR DL PBM SSWC DB IFV CH BH BR DL

a ¼ 0:1 112,280.39 0.680 0.633 80.76 2,785,474 1538.16 3,071,661 966,484,596 a ¼ 0:4 1,126,076.73 0.702 0.603 89.89 3,252,773 1794.57 3,190,236 985,036,405 a ¼ 0:7 113,534.33 0.721 0.572 94.93 3,493,723 1943.26 3,427,659 990,267,632

a ¼ 0:2 112,294.18 0.689 0.633 82.16 2,839,150 1620.84 3,081,277 891,819,746 a ¼ 0:5 112,677.69 0.703 0.591 92.26 3,304,476 1927.89 3,241,193 985,642,304 a ¼ 0:8 113,998.32 0.738 0.582 95.75 3,572,362 1954.23 3,487,593 990,526,537

a ¼ 0:3 112,579.43 0.698 0.578 91.26 3,241,791 1652.82 3,176,163 978,995,920 a ¼ 0:6 113,134.37 0.714 0.558 93.94 3,472,772 1933.27 3,327,259 988,526,317 a ¼ 0:9 114,292.15 0.742 0.778 97.15 3,839,150 2002.83 ¡3,561,277 991,819,746

The bold values are the best results.

Fig. 9.7 shows the illustration of the comparison in accuracies of above algorithms based on the values of the validity indices and the number of clusters. In these installations, we set a ¼ 0:9 and m ¼ 2. The average results stated in Table 9.12 also ascertain that the process of selection the most appropriate parameters and the advantages of proposed algorithms as well. In the case of changing values of parameters (b1, b2, b3) in SSFC-FS. The establishment for different sets of (b1, b2, b3) includes: Instance 1: Instance 2: Instance 3: Instance 4: Instance 5:

(b1 > b3 > b2): (b3 > b2 > b1): (b2 > b1 > b3): (b3 > b1 > b2): (b1 > b2 > b3):

(b1 ¼ 0.6, b2 ¼ 0.1, b3 ¼ 0.3). (b1 ¼ 0.1, b2 ¼ 0.3, b3 ¼ 0.6). (b1 ¼ 0.3, b2 ¼ 0.6, b3 ¼ 0.1). (b1 ¼ 0.3, b2 ¼ 0.1, b3 ¼ 0.6). (b1 ¼ 0.6, b2 ¼ 0.3, b3 ¼ 0.1).

286 Chapter 9 (A)

(B)

(C)

(D)

(E)

(F)

(G)

(H)

Figure 9.7 The accuracy of mentioned methods by the changes of number of clusters. (A) The PBM’s results. (B) The SSWC’s results. (C) The IFV’s results. (D) The DB’s results. (E) The CH’s results. (F) The BH’s results. (G) The BR’s results. (H) The DL’s results.

Instance 6: (b2 > b3 > b1): (b1 ¼ 0.1, b2 ¼ 0.6, b3 ¼ 0.3). In Table 9.12 the results show that SSFC-FS is suitable with the instance 3 (b2 > b1 > b3): (b1 ¼ 0.3, b2 ¼ 0.6, b3 ¼ 0.1).

2. Conclusions This chapter summarizes the authors’ results in the segmentation problem for dental X-ray images. Different semisupervised methods that use dental X-ray image features to improve clustering progress to get the best results were presented. Based on achieved results, a decision-making support mechanism was presented with the most suitable parameters. With each input radiography, the segmentation progress is applied to get the clustering results. Based on this output, all the necessary steps are performed to obtain the best result of diagnosing by using various methods. All methods mentioned in this chapter are implemented and tested by a dataset that contains 66 radiography images. The detailed numerical results were shown in our publications, respectively. In this chapter, we just give the general achievements. The evaluation and comparison with other methods are also given in this chapter. The results stated in Tables 9.8e9.12 prove the higher performance of proposed methods.

Table 9.12: The average values of SSFC-FS using different values of (b1, b2, b3). Instance 1 PBM IFV CH BR DL DB SSWC BH

99,509.74 85.31 3,354,359 3,767,383 1,031,946,025 2.125 0.760 3166.07

Instance 2 137,428.08 113.46 4,180,787 2,727,826 1,207,344,675 0.841 0.760 3623.02

The bold values show the best ones in each validity index.

Instance 3 129,497.12 113.68 4,309,572 3,138,495 1,615,031,877 1.666 0.857 5298.38

Instance 4 98,837.42 101.73 4,426,838 2,832,341 1,378,747,995 0.971 0.785 3944.51

Instance 5 98,543.57 78.39 3,645,016 ¡4,014,768 1,014,069,296 1.903 0.745 3076.74

Instance 6 96,674.93 94.90 4,237,292 3,280,447 1,550,466,044 1.772 0.893 5190.08

288 Chapter 9 From the results of this research, there are some approaches that can be done in the future. We would like to develop efficient clustering methods for different problems. The models presented in this chapter can be applied in other kinds of images. Moreover, these models also can be integrated to other techniques such as neural network or deep learning. The application of these models can also be extended to diagnose support problems.

Acknowledgments The author (Do Nang Toan) would like to thank the supports of the Project VAST01.09/17-18 entitled: “Research and development of techniques to support Museum exhibits based on Virtual reality technology”.

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Index ‘Note: Page numbers followed by “f” indicate figures and “t” indicate tables.’

A AAV vectors. See Adenoassociated viral vectors (AAV vectors) ACROBOT platform, 131 Active contour method, 162 Actuators, 208 Ada-Boost, 172 Adaptability, 138 Adaptive histogram thresholds, 162 Adaptive resonance theory mapping (ARTMAP), 79 ADCs. See Automated converters (ADCs) Additional information for SSFCFS method (SSFC-FSAI method), 275, 280 Addressing, 138e139 Adeno-associated viral vectors (AAV vectors), 143 ADV. See Advanced data visualization (ADV) Advanced data visualization (ADV), 4 Advanced medical imaging, 122 AEGLE enterprise, 115 AESOP platform, 131 Age regression equation, 29 Aggregated data, 7 AI. See Artificial intelligence (AI) AM. See Amplitude modulation (AM) Amazon, 106 Amazon web services, 238 Amplitude modulation (AM), 90 Amplitude shift key (ASK), 90 Analog signals, 76e77 Analysis of variance (ANOVA), 168e169

ANN. See Artificial Neural Network (ANN) Anomaly location, 114 ANOVA. See Analysis of variance (ANOVA) ANT, 136 Antenna communication, 90 Architectural Working Group (AWG), 187e188 C4ISR, 187e188 Arduino programming, 16 Artificial intelligence (AI), 75, 95e96, 124, 132e133 in telemedicine, 127e134 Artificial Neural Network (ANN), 79, 169, 175 ARTMAP. See Adaptive resonance theory mapping (ARTMAP) Asimov (Android-based mobile application), 130 ASK. See Amplitude shift key (ASK) ATWIN Quad-band GPRS/GSM shield, 16 Automated assistive devices, 96 Automated biopsy system, 94 Automated Endoscopic System for Optimal Positioning robotic surgical system (AESOP robotic surgical system), 82e83, 88f Automated human cortical bone haversian canal histomorphometric comparison system automated comparison system, 49e50 comparison test selection, 49e50, 50f

291

automated system design, 50e52 automated cortical bone analyzer system, 51f automated race histological haversian canal parameter, 51f comparison test execution section, 52f difficulties in sample preparation, 40e44 broken section, 42e43 dirty specimen, 43 fragile bone specimen, 43e44 thick bone slice, 42 trapped air bubbles in glass sample, 40e42 uneven thickness, 42 image acquisition, 44e46 regions selected for, 45f inclusion and exclusion criteria, 48 microstructural parameter selection, 46e48, 46f race comparison, 64 without age groups, 58e64 descriptive statistics, 59t hca, 60e61, 60t, 61f hcm, 59e60, 60f, 60t hcn, 62, 63f, 63t hcp, 61e62, 62t, 63f hcpar, 62e64, 64f, 64t hcr, 61, 61t, 62f SW-test, 59t sample collection, 31e33, 32t long bones of human skeleton, 32f number of collected specimen with respect to bone type, 33t sample preparation, 33e40

Index Automated human cortical bone haversian canal histomorphometric comparison system (Continued) glass slide mounting, 39e40, 40f left and right-hand side femur and fibula long bone specimen, 33f sectioning of bone specimen, 34 slice cutting using mini hacksaw and G-clamp, 35f specimen defatting, 34, 35f specimen grinding and polishing, 36e38, 39f sex comparison, 57e58 without age groups, 52e58 descriptive statistics of sex comparison, 52t hca, 54, 54t, 55f, 55t hcm, 53, 53t, 54f hcn, 56e57, 57f, 57t hcp, 56, 56f, 56t hcpar, 57, 58t hcr, 54, 55f SW-test, 53t statistical tests, 48e49 Automatic alarm circuits, 94 Automatic malaria parasite detection technique, 175 Automatic pill dispensing system, 95 Automatic wireless sensor networking in biomedical instrumentation, 84e85 Automation in biomedical instrumentation, 71 in field of biomedical instrumentation, 78e80 automation in medical instruments, 80 in telerobotic surgeries, 80e81 AutoPulse platform, 131 AVRO, 6, 232 AWG. See Architectural Working Group (AWG)

B BAN. See Body Area Network (BAN) Base station, 85 BD. See Big data (BD) BDA. See Big data analytics (BDA) Behavioral data, 4 Bell function, 276 Big data (BD), 11e12, 104e106, 110e111, 139e140, 154e155, 222e225 analysis for malaria prediction clinical diagnosis, 155 computerized diagnosis, 157e175 diagnosis techniques, 155e175, 155f disease prediction model based on big data analysis, 154e155 manual microscopic examination of blood smear, 155e156 QBC, 156 RDT, 156 analytic methods in health care, 111e113 application to medical industry, 224e225 EHR, 224 evidence-based medicine, 225 fraud detection, 225 hospital readmissions, 225 in medical domain, 224 real-time alerts, 225 applications in fields, 105f architecture of large-scale platform to developing predictive model, 230e232 decision making tools and logic implementation in, 113e114 description of contribution, 107t discovery imaging indicators, 128f enhancing stem cell research and tissue engineering, 144e145 ethical issues, 239e246

292

ethical themes, 240e246 health care and four vs. of big data, 225e229 in health care development, 110e111 impact, 236e239 different type database repository and web site, 237t examples to complex biomedical information, 237e238 personalized medicines, 239, 239t model through big data analytics, 232e236 functional network algorithm, 235e236 predictive modeling architecture through largescale platform, 233e235 of nanotechnology to nanomedicine, 145e147 platform to, 233f Avro, 232 Cassandra, 232 HBase, 232 HDFS, 231 Hive, 232 Jaql, 232 Map Reduce, 231 PIG and PIGLatin, 232 quality assessment model in, 114e115 SI in big data health care, 205e212 adding semantic annotations to data, 208 experiments and results, 208e212 use case scenario of SI, 209f types nonprimitive big data analytics, 231 primitive big data, 230e231 workflow for biomedical image analysis, 124e127, 125f Bio sensors, 76, 77f Bioadhesive microspheres, 148e149

Index Biocurators, 223 Bioengineering, 74e75 Biomaterials, 75e76 Biomechanics, 74, 76 Biomedical applications, 75e76 of wireless sensor networking, 85e86 Biomedical engineering, 70, 72 applications, 83e84 automatic instrumentation in, 70 automation in telerobotic surgeries, 80e81 Bluetooth communication, 87e88 network topology, 87 robotic surgeries, 81e83 selecting RF transceivers, 90e91 sensing technologies, 88e90 Biomedical engineers, 72 Biomedical field, 77e78 Biomedical imaging, 76 big data workflow for, 124e127, 125f Biomedical instrumentation, 70e78 advancements and applications in, 91e97 applications of automation in, 96e97 automatic wireless sensor networking in, 84e85 automation in field of, 78e80 recent advancements and applications in, 91e97 Biomedical optics, 73 Biomedical technologists, 72 Biometric data, 11 Bionics, 76 biomedical instrumentation in, 94 Biosensors, 75, 111e112 Biotechnological advances, 123, 139e149 big data enhancing stem cell research and tissue engineering, 144e145 big data of nanotechnology to nanomedicine, 145e147

diffusion magnetic resonance imaging, 142f gene therapy, 143 neuroscience and brain research, 141e143 new drug discovery and drug delivery systems, 147e149 “Blanket” consent mechanisms, 242 BLE link Bee Bluetooth module, 88 BLE mini Bluetooth module, 88 Blood smear manual microscopic examination of, 155e156 preprocessing of image, 158e159, 161t Blue SMiRF Bluetooth module, 88 Bluetooth, 136 communication, 87e88 mate Bluetooth module, 88 modules used for biomedical applications, 88 BMI. See Brainemachine interfaces (BMI) Body Area Network (BAN), 85e86, 196e197 Bone fragments of burned bison at Mile Canyon, 26e27, 27f Bone slice thick, 42 uneven thickness, 42 BRAIN Initiative. See Brain Research Through Advancing Innovative Neurotechnologies Initiative (BRAIN Initiative) Brain research, 123, 141e143 Brain Research Through Advancing Innovative Neurotechnologies Initiative (BRAIN Initiative), 141e142 Brainemachine interfaces (BMI), 79 “Broad” consent mechanisms, 242

293

Broken section, 42e43, 44f BSN. See Body Sensor Network (BSN) Business logic, 104

C C-leg (Otto Bock) platform, 131 CAC. See Controller Application Communication (CAC) Calibration, 78 Cancer Moonshot Task Force report, 145 Cancer technology, 73 Capsule endoscopy, 92, 93f Cardiac robot-assisted surgery, 132 Cardiovascular technology, 73 Casandra file system (CFS), 5 Case arrangement quality character (CCQC), 114 Cassandra system, 232 Caucasoid skull, 27, 28f CC. See Cloud computing (CC) CCQC. See Case arrangement quality character (CCQC) Cellular identities, 144 CEP. See Complex event processing (CEP) CFS. See Casandra file system (CFS) CGM system. See Continuous glucose monitoring system (CGM system) Chip-enabled prosthetics, 134 Circular Hough transform, 174 Cisco health care solutions, 238 Clinical engineering, 76 biomedical instrumentation in, 95 Clinical text mining, 3 Cloud computing (CC), 6, 191, 193e194, 199, 239 cloud layer, 8e9 Cloud Service Layer, 137 cloud service module, 202 cloud-based infrastructures, 199e200 cloud-enabled pillow robot, 133e134

Index Clustering methods, 255 CoAP protocol, 198e199 Coatings, 75e76 Color channel histogram, 164e165 Common Service Description Language (CSDL), 187e188 Communication interface, 90 modes of wearable devices in IoT, 135e136, 137f long distance communication, 136 short distance, 136 very short distance, 136 system, 85 Complex event processing (CEP), 5 Computer assisted diagnosis systems, 153e154 Computer tomography (CT), 3, 72, 80, 92 Computer-aided malaria diagnosis, 172e175 diagnosis methods using thin blood smear, 173t Computer-guided diagnostic systems, 139e140, 140f Computerized DIP technique, 153e154 Computerized malaria diagnosis, 157e175 database collection setup, 157e158, 158f preprocessing of blood smear image, 158e159 segmentation, 159e164 Computerized technologies, 77 Confidence, 78 Consent, 240e242 “broad” and “blanket” consent mechanisms, 242 single-instance, 242 tired, 242 Context-sensitive approach, 243 Continuous glucose monitoring system (CGM system), 9 Control mechanism, 96 Controller system, 85

CPS. See Cyber-physical system (CPS) Cranium, 28, 29f CRISPR/Cas9 genome editing tools, 144 CSDL. See Common Service Description Language (CSDL) CT. See Computer tomography (CT) Cyber physical cloud computing and health care approaches, 111 Cyber-physical system (CPS), 111 Cyber-physical therapy system (T-CPS), 111 CyberKnife platform, 131

D da Vinci Surgical System, 82, 86f Da-Vinci robotic platform, 131 Data clustering, 255 exchange systems, 200 integration, 200 mapping technologies for data models, 200 mining methods, 254 normality, 49 protection, 242 provider, 205e208 rate, 90 science, 222e223 technology, 110e111 Datagram Transport Layer Security (DTLS), 199 Datasets, 2 DCS. See Distributed Control System (DCS) DDE. See Distributed Decision Engine (DDE) DDS. See Drug delivery systems (DDS) Decision making tools, 113e114 Delaunay triangulation-based concavity point detection method, 162 Dell Health Care solutions, 237 Demographic data, 11

294

Demographic profiles, 26e27 Dental image segmentation model, 263e265 Dental X-ray image, 254, 275 dataset data description, 282 defining features, 282 validity indices and evaluation criteria, 283 Department of vector borne diseases (DVBD), 154e155 Device layer, 8e9 Diagnosis techniques for malaria, 155e175, 155f Diaphragm, 73 Digital image processing technique (DIP technique), 153e154 Digital microscope imaging system, 153e154, 157 DinoEye digital eyepiece microscope camera, 44 DIP technique. See Digital image processing technique (DIP technique) Dirty specimen, 43 Distributed Control System (DCS), 79 Distributed Decision Engine (DDE), 106e107 DNA polymorphisms, 223 DoD. See US Department of Defense (DoD) Driver drowsiness detection, Smartwatch-based wearable EEG system for, 137 Drug delivery systems (DDS), 147e149 Drug discovery systems, 147e149 Dry grinding process, 36, 36f DSSs. See Decision Support Systems (DSSs) DTLS. See Datagram Transport Layer Security (DTLS) DVBD. See Department of vector borne diseases (DVBD)

Index E

F

E-health, 110 Easy device management, 10 ECG, 72, 74, 85, 93, 106e107 derived respiration rate, 89 EEG. See Electroencephalogram (EEG) eHealth, 191 EHR. See Electronic health records (EHR) Electrical safety analyzers, 97 Electroencephalogram (EEG), 74, 79, 93, 130, 141e142 Electronic health mind services, 106e107 Electronic health records (EHR), 2e3, 110e111, 224 Electronic medical records (EMRs), 130 Electronic Product Codes (EPC), 186 Embedding system, 74 EMG, 93 Emission tomography imaging, 80 EMRs. See Electronic medical records (EMRs) EPC. See Electronic Product Codes (EPC) Erythrocyte, 153e154, 162 analysis system, 175e176 classification model, 171 segmentation, 160e162 eSFCM. See Semisupervised entropy regularized fuzzy clustering (eSFCM) Ethical themes of big data, 241t consent, 240e242 data protection, 242 epistemology, 244e245 objectivity, 245e246 ownership, 243e244 privacy, 243 Euclidean distance, 169 classifier, 172 Evidence-based medicine, 225

f-MRI. See Functional Magnetic Resonance Imaging (f-MRI) Fault-tolerant medical data services, 13e14 FCM method. See Fuzzy C-means method (FCM method) FDA. See US Food and Drug Administration (FDA) Feed-forward back-propagation network, 169e171 Floating microspheres, 148e149 FM. See Frequency modulation (FM) Fog layer, 8e9 Four Vs. See Volume, variety, velocity, and veracity (Four Vs) Fragile bone specimen, 43e44, 45f Fraud detection, 225 Frequency modulation (FM), 90 Frequency shift key (FSK), 90 Fresnel function, 277 FS. See Fuzzy satisficing method (FS) FSK. See Frequency shift key (FSK) Functional Magnetic Resonance Imaging (f-MRI), 72, 92, 141e142 Functional network, 234e235 algorithm, 235e236 Fuzzy C-means method (FCM method), 254e255 Fuzzy clustering, 255e256 function, 278 methods, 252 Fuzzy satisficing method (FS), 267e272

G Gaussian function, 276 GE health care life science, 238 Gene therapy, 143 General robot-assisted surgery, 132

295

General-purpose interface bus (GPIB), 90 Genetic algorithm, 13e14, 168e169 Genomic data, 3 Genomic database, 3 Giemsa method, 155e156 Glass slide mounting, 39e40, 40fe41f GLCM. See Gray level co-occurrence matrix (GLCM) Global System for Mobile (GSM), 116 Glucometer, 70e71 Google, 106 GPIB. See General-purpose interface bus (GPIB) GPUs. See Graphics processing units (GPUs) Grand View Research, 8 Graphics processing units (GPUs), 239 Gray level co-occurrence matrix (GLCM), 164e165 Gray level run length matrix (GRLM), 164e165 Grid-Wise Architectural Council (GWAC), 188 GRLM. See Gray level run length matrix (GRLM) GSM. See Global System for Mobile (GSM) Gudermannian function, 277 GUIs. See Graphical UIs (GUIs) GWAC. See Grid-Wise Architectural Council (GWAC) Gynecology, 132

H H-max distance measurement, 252e253 Hadoop distributed file system (HDFS), 5, 18e19, 230e231, 233e234 Hard clustering methods, 252

Index Haversian canal in observed region (hcpar), 48 race comparison, 62e64, 64f, 64t sex comparison, 57, 58t Haversian canal number (hcn), 46 race comparison, 62, 63f, 63t sex comparison, 56e57, 57f, 57t Haversian canal radius (hcr), 46 race comparison, 61, 61t, 62f sex comparison, 54, 55f HBase system, 232 HBD. See Health Care BD (HBD) HC-05 Bluetooth module, 88 HC-06 Bluetooth module, 88 hca. See Total area covered by haversian canal (hca) hcm. See Mean haversian canal area (hcm) hcn. See Haversian canal number (hcn) hcp. See Mean haversian canal perimeter (hcp) hcpar. See Haversian canal in observed region (hcpar); Percentage area covered by haversian canal (hcpar) hcr. See Haversian canal radius (hcr) HDFS. See Hadoop distributed file system (HDFS) Health care big data analytics in, 12f with big data challenges, 6e7 issues relating to policy and fiscal factors, 6e7 issues relating to technology, 7 data sources, 2e4, 3f behavioral data, 4 clinical text mining, 3 EHR, 2e3 genomic data, 3 medical imaging data, 3 diagnostic chain, 129f and four Vs of big data, 225e229

industry, 10e11 IoT for, 8e9 architectural elements, 9f challenges, 9e10 coverage analysis in health care services, 8f monitoring frameworks, 115e116 parameters, 126e127 robotics in, 128e129 system availability of health care information in social media, 108 big data analytic methods in, 111e113 ICT and big data in, 110e111 smart applications, 108e110 technology modernization and quality as challenge in, 106e107 Health Care BD (HBD), 110e111 Health care Information Systems (HISs), 106 Health-mind part, 110e111 Hereditary disorders, 71e72 High-throughput analytics techniques, 223 HISs. See Health care Information Systems (HISs) Histogram, 165 thresholding, 175 Histogram thresholding with morphological approach, 162 Histological methods, 29 HITECH Act, 139 Hive, 4, 232 Hive Query Language (HQL), 232 HMI. See Human Machine Interface (HMI) Home automation model, 201 Home based rehabilitation, 77 Hospital readmissions, 225 Hospital System, 197e199 hPSC. See Human pluripotent stem cell (hPSC)

296

HQL. See Hive Query Language (HQL) Human Machine Interface (HMI), 79, 139 Human pluripotent stem cell (hPSC), 144 Human skull, 28, 29f morphological changes in, 30f Human-generated data, 11 Hybrid classifier, 172 clustering algorithm, 253 segmentation algorithm, 160e162 Hyperbolic Sine function, 277 IaaS. See Infrastructure as a Service (IaaS)

I iBlock platform, 131 IBM Health Care And Life Sciences Company, 237 ICT in health care development, 110e111 IEEE 802.15.4 standard (ZigBee), 86 sender and receiver with heart beat sensor, 16, 17f IFV index, 272 Image processing OTSU threshold algorithm in, 258e260 techniques, 29e30 Image segmentation, 159, 253e254 combination of eSFCM and OTSU in, 258 IMP. See Intelligent Medical Packaging (IMP) Implant, 73 biomedical instrumentation in, 94 Implantable pacemaker design, 73, 74f IMS. See Intelligent Medical Server (IMS) In-vitro instrument, 91e92 In-vivo instrument, 91e92 Infected erythrocyte, 162e164 Information

Index extraction from skeletal remains, 26e27 ingestion, 10 technology, 71e72 Informative analytics, 10 Informed consent, 240e241 Infrastructure as a Service (IaaS), 193 InnoMotion platform, 131 Innovations in medical instruments, 71 Instrument, 72 Instrumentation, 72, 76 engineering, 72 technologists, 72 Insurance fraud, 225 Intel Galileo, 16 Intel Galileo Gen2, 16, 17f, 104e105 SIM card integrated to, 18f SMT package inserted into Intel Galileo with PCB antenna, 18f Intel health care, 238 Intelligent health cloud, 205e208 Intelligent Medical Server (IMS), 196e197 Interactive FS method, 275 Internet, 196 Internet and Communication Technologies, 187 Internet of Robotic Things (IoRT), 127e128, 133e134 Internet of Things (IoT), 7, 71e72, 91, 104e106, 122e124, 134e141, 186e187, 190e191 communication modes of wearable devices in, 135e136 for health care, 8e9 architectural elements, 9f challenges, 9e10 coverage analysis of IoT in health care services, 8f health care, SI in, 202e205 health care conditions and, 190t, 192te193t

IoT-based intelligent wallet system, 13e14 IoT-based smart home system, 201 monitoring model, 13e14 smart applications relating to health care systems using, 108e110 Internet-connected smart local access network, 8e9 Invasive biosensors for WSN, 89 IoRT. See Internet of Robotic Things (IoRT) IoT. See Internet of Things (IoT) IP-based LPWNs. See Web Protocol-based LPWNs (IP-based LPWNs) ITEAD BT Bluetooth module, 88

J JAQL language, 6, 232 Jaswant Singh Bhattacharya method (JSB method), 155e156 JY-MCU Bluetooth module, 88

K K-closest neighbor (KNN), 114 K-means method, 254 k-Nearest neighbor (k-NN), 169 Key performance indicators (KPI), 5 Knee joints in total knee replacement, 94 KUKA KR6 (Industrial robotic arm), 130

L LA method. See Lagrange method (LA method) Laboratory automation, 80 Laboratory instruments, 95 Lagrange method (LA method), 272 Lagrange multiplier method, 255, 261, 273 segmentation problem solving using, 265e266

297

Lambda design restrictions, 106 Laser surgeries, 93 LBP. See Local binary pattern (LBP) Leishman method, 155e156 Lentiviral vectors, 143 Lentiviruses, 143 Levels of Information Systems Interoperability (LISI), 187e188 Life supporting equipment, 74 LISI. See Levels of Information Systems Interoperability (LISI) Local binary pattern (LBP), 164e165, 261e262 6LoWPAN. See IPv6 over LowPower Wireless Personal Area Networks (6LoWPAN) LPWN. See Low-Power Wireless Network (LPWN)

M MAC. See Message Authentication Code (MAC) Machine learning (ML), 124, 168e169 algorithms, 12 ML-system model, 13e14 Machine learning library (ML Lib), 234e235 Machine-to-machine device generated data, 11 Magnetic microspheres, 148e149 Magnetic resonance imaging (MRI), 72e73, 80, 122 Mahout, 6 Malaria, 153e154 Malaria infection identification, 169e172 Ada-Boost, 172 computer-aided malaria diagnosis, 172e175 Euclidean distance classifier, 172 hybrid classifier, 172 k-Nearest neighbor, 169 multivariate regression, 171

Index Malaria infection identification (Continued) Naı¨ve Bayes theorem, 171 neural network, 169e171 performance of state-of art classification methodology, 170t SVM, 171 Malaria-infected erythrocytes, 174 Malaysian population, ethnic composition of, 27, 28f Management information system (MIS), 132e133 Mandible, 28, 29f Mann Whitney U-Test, 49 Manual measurements, 29e30 Manual microscopic evaluation (MME), 153e154 Map Reduce, 231 programming, 19 system, 5 technique, 234e235 Mapping technologies for data models, 200 Marker-controlled watershed algorithm, 160e162 MATLAB, 201 Maximum transmission distance, 90 Mean haversian canal area (hcm), 46e47 perimeter, 48 race comparison, 59e60, 60f, 60t sex comparison, 53, 53t, 54f Mean haversian canal perimeter (hcp), 48 race comparison, 61e62, 62t, 63f sex comparison, 56, 56f, 56t Measurand, 73 Measurement, 77e78 resolution, 90 techniques, 73 MedDRA repository, 208e209, 211e212 Medical device, 73 biomedical instrumentation in, 93

Medical domain, big data in, 224 Medical imaging, 76 biomedical instrumentation in, 92 data, 3 Medical instruments, 71e73 automation in, 80 Medical IoT, evolution of big data in, 10e12 mHealth care services. See Mobile health care services (mHealth care services) Microradiography, 29e30 Microscopic feature extraction, 164e165, 166te168t Microscopic image analysis, 153e154 Microspheres, 148e149, 148f Microstructures, 30 imaging location, bone type, and area selected by researchers, 31t Minerva platform, 131 MIS. See Management information system (MIS) Mixed function, 278 ML. See Machine learning (ML) ML Lib. See Machine learning library (ML Lib) MLP. See Multilayer Perceptron (MLP) MME. See Manual microscopic evaluation (MME) Mobile health care services (mHealth care services), 106e107 Mobile support malaria diagnosis system, 165 Model selection, 235e236 Mongoloid skull, 27, 28f Motion Control, 79 Motion imaging, 92 Mounting medium, 40e42 MRI. See Magnetic resonance imaging (MRI) Mucoadhesive microspheres, 148e149 Multiclass SVM, 171

298

Multilayer Perceptron (MLP), 169 Multiomics strategies, 144 Multivariate regression, 171 Myoelectric controlled prosthetic arm, 94

N Naı¨ve Bayes order calculation (NB order calculation), 112e113 Naı¨ve Bayes theorem, 169, 171 Namibia HIS, 106 Naming, 138e139 Nanoparticles (NPs), 146e147 Nanotechnology, 145e146 Nanotherapeutics, 146e147 National eHealth strategies, 193t National Institutes of Health (NIH), 146e147 Navio PFS platform, 131 NB order calculation. See Naı¨ve Bayes order calculation (NB order calculation) Near field communication (NFC), 136 Negroid skull, 27, 28f Netflix, 106 NetSim Simulator, 106 Network Information Service (NIS), 106e107 Network topology, 87 Neural engineering, 75 biomedical instrumentation in, 95e96 Neural network, 169e171 malaria prediction model, 154e155 Neurological techniques, 73 Neurologization of mental health information, 141e142 Neuroscience, 141e143 spatiotemporal realm, 141f NFC. See Near field communication (NFC) NI LabVIEW, 116 NIH. See National Institutes of Health (NIH) Nikon Eclipse Ts100 microscope, 44

Index Niobe platform, 131 NIS. See Network Information Service (NIS) NNI. See US National Nanotechnology Initiative (NNI) No-Structure Query Language system (No-SQL system), 232 Noninvasive bio sensors for WSN, 89 Nonparametric u-test, 49e50, 62e64 Nonprimitive big data analytics, 231 Normalized cut segmentation method, 162e164 Novalis with TrueBeam STx platform, 131 Novel biosensing approaches, 111e112 NPs. See Nanoparticles (NPs) Null hypothesis, 48e49

O Objectivity, 245e246 Observed region, 46 OIC. See Open internet Consortium (OIC) OLAP. See Online analytics processing (OLAP) OLTP. See Online transaction processing (OLTP) On-off key (OOK), 90 OneM2M model, 200 Online analytics processing (OLAP), 5 Online transaction processing (OLTP), 5 OOK. See On-off key (OOK) Open internet Consortium (OIC), 188 Open system interconnect layered architecture (OSI layered architecture), 86, 95f Operating frequency, 90 Optogenetics, 141e142 Oracle life sciences, 238 ORTHODOC platform, 131 Orthopedic technology, 73

OSI layered architecture. See Open system interconnect layered architecture (OSI layered architecture) Osteon system, 48 OTSU combination of eSFCM and OTSU in image segmentation, 258, 259f threshold algorithm in image processing, 258e260 Otsu’s thresholding, 160e164, 175e176 Ownership, 243e244

P PaaS. See Platform as a Service (PaaS) PAC. See Programmed Automation Controller (PAC) PAN. See Personal Area Network (PAN) Parametric learning, 233 Parasite identification methodology, 175 segmentation, 153e154, 162e164, 175e176 Patch clamping, 141e142 Pathfinder platform, 131 Pattern recognition, 168e169 PB-KNN. See Pruning-based KNearest Neighbor (PBKNN) PDAs. See Personal digital assistants (PDAs) Percentage area covered by haversian canal (hcpar). See Haversian canal in observed region (hcpar) Personal Area Network (PAN), 85e86 Personal digital assistants (PDAs), 194e195 Personal health record (PHR), 111 Personal monitoring devices (PMD), 197 Personalized medicines, 239, 239t

299

PET. See Positron emission tomography (PET) PET-CT. See Positron emission tomography-computed tomography (PET-CT) Phase shift key (PSK), 90 Phenogenotypic data, 145 PHR. See Personal health record (PHR) Picture Archival & Communication Systems, 3 Piezo motor-powered MRI robot, 93 PIG programming language, 232 PIGLatin, 232 Pixel classification with connected component labeling, 162 Plasmodium, 153e154 falciparum, 153e154, 164e165, 171e172, 174e175 malariae, 153e154, 172 ovale, 153e154, 164e165, 172 vivax, 153e154, 164e165, 171e172, 174e175 Platform as a Service (PaaS), 193 PLC. See Programmed Logic Controller (PLC) PMD. See Personal monitoring devices (PMD) Poisson distribution thresholding, 162 Portable health mind, 106e107 Positron emission tomography (PET), 72, 92 Positron emission tomographycomputed tomography (PET-CT), 124 Posttraumatic stress disorder (PTSD), 137 Predictive modeling architecture through large-scale platform, 233e235 Map Reduce, 234e235 Preprocessing of blood smear image, 158e159 Presto, 4 Primary data, 2 Primitive big data, 230e231

Index Principal component analysis, 168e169 Privacy, 139, 243 Probabilistic k-means clustering approach, 175 PROBOT platform, 131 Processing system. See Controller system Programmed Automation Controller (PAC), 79 Programmed Logic Controller (PLC), 79 Pruning-based K-Nearest Neighbor (PB-KNN), 114 PSK. See Phase shift key (PSK) PTSD. See Posttraumatic stress disorder (PTSD) Pulse rate sensor, 89

Q Qualitative data, 2 Quality assessment model in big data, 114e115 Quantitative buffy coat (QBC), 153e154, 156 Quantitative data, 2

R Radial-based cell formation (RCF), 162 Radiation safety, 97 Radio frequency (RF), 90 selecting RF transceivers, 90e91 safety issues, 91 specifications, 90 Radio Frequency Identification (RFID), 186 Radio immobilization therapy microspheres, 148e149 Radiology datasets, 126e127 Radius of each haversian canal, 47, 47f Rapid diagnostic tests (RDTs), 153e154, 156 Raspberry Pi, 201 RCF. See Radial-based cell formation (RCF)

RDF. See Resource Description Framework (RDF) RDTs. See Rapid diagnostic tests (RDTs) Real-time alerts, 225 Real-time big data analytics of IoT-based health care monitoring system, 15e21. See also Big data analytics average data transmission time, 22t body pressure with Hive query command, 20f components and methods, 16e19 flow chart for alert, 21f health-monitoring interface, 21f IoT architecture, 16f results, 19e21 threshold data of heart beat rate alert signals, 22t Real-time visualization, 92 Red-green-blue (RGB), 261e262 Reduced risk, 10 Regression methods, 12 Rehabilitation engineering, biomedical instrumentation in, 96 Reimbursement data model, 223 Remodeling process of human bone, 30 Remote, surgery. See Telesurgery Renaissance platform, 131 Representational State Transfer (REST), 199e200 Resource Description Framework (RDF), 189, 203e208 Respiration rate sensor, 89 REST. See Representational State Transfer (REST) RESTful CoAP protocol, 190, 194, 198e199, 199f RESTful principles, 186e187, 201 RF. See Radio frequency (RF) RFID. See Radio Frequency Identification (RFID) RGB. See Red-green-blue (RGB)

300

RIO robotic arm platform, 131 ROBODOC platform, 131 ROBOTIC arms, 127e128 Robotic health care, instruments for, 131f Robotic surgeries, 71, 81e83. See also Type-3 tele surgical robotic surgery system type-1 supervisory surgery systems, 81e82 type-2 shared-control robotic surgery systems, 82 type-3 tele surgical robotic surgery system, 82e83 Robotic(s), 80e81 applications, 132 characteristics of robotic approach, 130f in health care, 128e129 history, 129e130 technology, 128e130 areas, 133t in telemedicine, 127e134 RS 232: TTL Bluetooth module, 88 Ruler Faisal Specialist Hospital and Research Center, 112

S SaaS. See Software as a Service (SaaS) Safety, 75, 97 Scalability, 138 Secondary data, 2 Sectioning of bone specimen, 34 Security, 201e202 systems, 75 Segmentation, 159e164 erythrocyte, 160e162 feature selection, 168e169 infected erythrocyte and parasite segmentation, 162e164 malaria infected region segmentation methodology, 163t malaria infection identification, 169e172 microscopic feature extraction, 164e165, 166te168t

Index performance with, 283e286 clustering algorithms results, 283e286 experiments in dental X-ray image dataset, 283 techniques, 175e176 Semantic annotations, 200e202 Semantic interoperability (SI), 186e187, 199e202 additional SI implementation tools, 215te217t in big data health care, 205e212 data integration and exchange systems, 200 implementation tools, 214te215t in IoT health care, 202e205 adding semantic annotations, 202e204 experiments and results, 204e205 use case scenario, 203f mapping technologies for data models, 200 ontologies and standards, 200 semantic annotations, 200e202 in smart city applications, 188f Semantic mediator, 205e208 Semistructured data, 11e12 Semisupervised clustering, 252 Semisupervised entropy regularized fuzzy clustering (eSFCM), 256e258 combination with OTSU in image segmentation, 258, 259f Semisupervised fuzzy clustering, 252e253, 256e258 semisupervised entropy regularized fuzzy clustering, 256e258 Semisupervised fuzzy clustering with spatial feature algorithm (SSFC-SC algorithm), 261, 263e266 advantages of the proposed algorithms, 275 average values of SSFC-FS using different values, 287t

defining suitable SSFC-FSAI, 275 framework, 275 set of additional information functions, 276e278 dental image segmentation model, 263e265 determining suitable additional information, 262 fuzzy satisficing method and, 267e271 general framework, 261f properties and consequences from solution analysis, 271e274 results of implementations and applications dental X-ray image dataset, 282e283 performance with segmentation methods, 283e286 segmentation problem solving using Lagrange multiplier, 265e266 Sensei X platform, 131 Sensing technologies, 88e90 invasive biosensors for WSN, 89 noninvasive bio sensors for WSN, 89 respiration rate sensor, 89 RF and antenna communication, 90 Sensitivity, 90 Sensor Web Empowerment (SWE), 202e203 Sensors, 4, 8e9, 72e73, 85, 208 Sensory input unit (SIU), 137 Sensory processing unit (SPU), 137 SensRcore, 136 Sex-based comparison, 29 ShapiroeWilk test (SW-test), 48e50, 53t SHES. See Smart health care execution system (SHES) SI. See Semantic interoperability (SI) SI module, 202 SIDER cloud platform, 208e209

301

Sigmoid function, 277 Simple connectivity, 9 Single Photon Emission Computed Tomography (SPECT), 72, 92 Single-instance consent, 242 Single-stage analysis system, 174 Sister Kenny Home Therapy system (SKOTEE), 130 SIU. See Sensory input unit (SIU) SKOTEE. See Sister Kenny Home Therapy system (SKOTEE) Smallest Univalue Segment Assimilating Nucleus filter (SUSAN filter), 158e159 Smart applications relating to health care systems using IoT, 108e110 Smart e-health care gateway, 13e14 Smart Flow Model, 103e104 Smart health care execution system (SHES), 106 Smart Health Monitoring System, 116 Smart home concept, 201 Smartwatch-based wearable EEG system for driver drowsiness detection, 137 SOA, 191 Sobel edge detection, 175 Social media, 11 Social media, availability of health care information in, 108 Soft clustering methods, 252 Software as a Service (SaaS), 193 Software-based designing, 75e76 Spark R, 234e235 SPARQL queries, 204e209, 211 Spatial function, 278 Specimen defatting, 34, 35f Specimen grinding and polishing, 36e38, 39f

Index SPECT. See Single Photon Emission Computed Tomography (SPECT) SPU. See Sensory processing unit (SPU) SQL. See Structure Query Language (SQL) SSFC-FSAI method. See Additional information for SSFC-FS method (SSFC-FSAI method) SSFC-SC algorithm. See Semisupervised fuzzy clustering with spatial feature algorithm (SSFCSC algorithm) Standards, 138 Stanmore Sculptor platform, 131 State of the art CC, 193e194 IoT, 190e191, 190t U-health care system, 194e199 Statistical tests, 48e49 based techniques, 168e169 Structural learning, 233 Structure Query Language (SQL), 232 Structured data, 11e12 Subjectivity, 104 Subsensor system, 85 Supervised classifier, 162e164 Supervised clustering, 252 Support vector machine (SVM), 169, 171 SVM-based parasitemia detection methodology, 175 Surgical robots, 80e81, 81t, 83 applications, 83e84, 83t future, 84 PROS and CONS, 83e84 SUSAN filter. See Smallest Univalue Segment Assimilating Nucleus filter (SUSAN filter) SVM. See Support vector machine (SVM) SW-test. See ShapiroeWilk test (SW-test) SWE. See Sensor Web Empowerment (SWE)

T T-CPS. See Cyber-physical therapy system (T-CPS) t-test, 49e50, 59e60 Tele-consultation, 127e128 Tele-education, 127e128 Tele-surgery, 82, 127e128, 131e132. See also Type3 tele surgical robotic surgery system instruments for, 132f Telelap ALF-X platform, 131 Telemedicine, 110, 122, 134. See also Tele-surgery AI and robotics in, 127e134 in various applications, 129f Telemonitoring, 127e128 Telerobotic surgeries, automation in, 80e81 surgical robots, 81 Text mining, 6 “Thingspeak” cloud, 18e19, 19f Thoracic robot-assisted surgery, 132 3D printers, 94 Threshold based segmentation, 162 Tired consent, 242 Tissue engineering, 75 biomedical instrumentation in, 93e94 Total area covered by haversian canal (hca), 47 race comparison, 60e61, 60t, 61f sex comparison, 54, 54t, 55f, 55t Total haversian canal area, 47 Traditional clustering methods, 252 Traffic management, 139 Trapped air bubbles in glass sample, 40e42 Triangle wave function, 278 Type-1 supervisory surgery systems, 81e82 Type-2 shared-control robotic surgery systems, 82 Type-3 tele surgical robotic surgery system, 82e83. See also Robotic surgeries

302

AESOP robotic surgical system, 82e83, 88f da Vinci Surgical System, 82, 86f ZRSS, 82, 87f

U U-health care systems. See Ubiquitous health care systems (U-health care systems) U-test, 57, 60e61 Ubiquitous health care systems (U-health care systems), 190e191, 194e199 auxiliary health care apps for smartphones, 195f BAN, 197 IMS, 197 traditional U-health care system architecture, 196f UI module. See User interface module (UI module) UKMMC. See Universiti Kebangsaan Malaysia Medical Center (UKMMC) Ultrasonic walking cane for visually challenged person, 96 Ultrasonography (USG), 124 Ultrasound, 72 Ultraviolet (UV), 156 Universal serial bus (USB), 90, 157e158 Universiti Kebangsaan Malaysia Medical Center (UKMMC), 31e32 Unstructured data, 11e12 Unsupervised classifiers, 162e164 Unsupervised clustering, 252 Urology, 132 US Department of Defense (DoD), 187e188 US Food and Drug Administration (FDA), 82e83 US National Nanotechnology Initiative (NNI), 145e146

Index USB. See Universal serial bus (USB) Use case scenario of SI, 202e203, 203f, 209f User interface module (UI module), 202 USG. See Ultrasonography (USG) UV. See Ultraviolet (UV)

V Vascular robot-assisted surgery, 132 Vertica, 4 Virtual Doctor Systems, 104 Visible Human Project, 139 Volume, variety, velocity, and veracity (Four Vs), 224 health care and big data, 225e229 Volume, velocity, and variety of big data (three vs. of big data), 11e12, 11f

W Watershed segmentation algorithm, 160e162 WBAN. See Wireless Body Area Network (WBAN) Wearable devices, 123, 134e139 applications, 137e138 architecture, 138f

classification and categories, 135, 135f communication modes of wearable devices in IoT, 135e136 research challenges and open issues, 138e139 working principles, 136e137 Web, 11 of health sensor things, 104e105 Wet grinding process, 37e38, 37f Wilcoxon Rank Sum Test, 49 Wireless Body Area Network (WBAN), 197 Wireless sensor networking, 84, 93f biomedical applications of, 85e86 IEEE 802.15.4 standard, 86 OSI layered architecture, 86, 95f WoT. See Web of things (WoT) WSN invasive biosensors for, 89 noninvasive bio sensors for, 89

X X-ray image segmentation, 252e254

303

advantages of new algorithm, 280e282 combination of eSFCM and OTSU in image segmentation, 258 defining appropriate additional information, 278e280 defining suitable additional information for SSFC-FS algorithm, 275 SSFC-SC, 261 theory background data clustering, 255 fuzzy clustering, 255e256 image segmentation problem, 253e254 semisupervised fuzzy clustering, 256e258 X-rays, 3, 72e73

Z Zack thresholding, 162e164, 174 ZEUS platform, 131 ZEUS robotic surgical system (ZRSS), 82, 87f ZigBee coordinator, 87 network layer, 87 technology, 201