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E-grāmata: U-Healthcare Monitoring Systems: Volume 1: Design and Applications

Edited by , Edited by (Associate Professor, Department of Computer Science and Engineering, Techno International New Town, Kolkata, India; Visiting Fellow , University of Reading, UK), Edited by (Assistant Professor and Head of Electronics and Electrical Communication), Edited by
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U-Healthcare Monitoring Systems: Volume One: Design and Applications focuses on designing efficient U-healthcare systems which require the integration and development of information technology service/facilities, wireless sensors technology, wireless communication tools, and localization techniques, along with health management monitoring, including increased commercialized service or trial services. These u-healthcare systems allow users to check and remotely manage the health conditions of their parents. Furthermore, context-aware service in u-healthcare systems includes a computer which provides an intelligent service based on the user’s different conditions by outlining appropriate information relevant to the user’s situation.

This volume will help engineers design sensors, wireless systems and wireless communication embedded systems to provide an integrated u-healthcare monitoring system. This volume provides readers with a solid basis in the design and applications of u-healthcare monitoring systems.

  • Provides a solid basis in the design and applications of the u-healthcare monitoring systems
  • Illustrates the concept of the u-healthcare monitoring system and its requirements, with a specific focus on the medical sensors and wireless sensors communication
  • Presents a multidisciplinary volume that includes different applications of the monitoring system which highlight the main features for biomedical sensor devices
Contributors xv
Preface xvii
Chapter 1 Wearable U-HRM Device for Rural Applications 1(14)
Mohd Imran
Mohammad Abdul Qadeer
1 Introduction
1(3)
2 U-Healthcare System in India
4(1)
3 Application
4(1)
4 Open Issues and Problems
5(1)
5 Requirements of a Healthcare System
6(1)
6 Requirement of Wearable Devices
7(1)
7 Implementation
7(1)
8 Measurement of Heart Rate and Body Temperature
8(3)
9 Discussion
11(1)
10 Conclusion and Future Trends
11(1)
Glossary
12(1)
References
12(3)
Chapter 2 A Robust Framework for Optimum Feature Extraction and Recognition of P300 from Raw EEG 15(22)
Saatvik Shah
Anirudha Kumar
Rajesh Kumar
Nilanjan Dey
1 Introduction
15(2)
2 Literature Survey
17(2)
3 The Framework
19(7)
3.1 Initialization
19(1)
3.2 Model Setup
20(1)
3.3 Postprocessor
21(4)
3.4 Classification
25(1)
4 Results and Discussion
26(6)
4.1 The Dataset
27(1)
4.2 Framework Results
27(5)
5 Conclusion and Future Work
32(1)
References
33(4)
Chapter 3 Medical Image Diagnosis for Disease Detection: A Deep Learning Approach 37(24)
Mrudang D. Pandya
Parth D. Shah
Sunil Jardosh
1 Introduction
37(3)
1.1 Related Work
39(1)
2 Requirement of Deep Learning Over Machine Learning
40(12)
2.1 Fundamental Deep Learning Architectures
41(11)
3 Implementation Environment
52(4)
3.1 Toolkit Selection/Evaluation Criteria
53(1)
3.2 Tools and Technology Available for Deep Learning
53(1)
3.3 Deep Learning Framework Popularity Levels
53(3)
4 Applicability of Deep Learning in Field of Medical Image Processing
56(1)
4.1 Current Research Applications in the Field of Medical Image Processing
56(1)
5 Hybrid Architectures of Deep Learning in the Field of Medical Image Processing
57(1)
6 Challenges of Deep Learning in the Fields of Medical Imagining
58(1)
7 Conclusion
59(1)
References
59(1)
Further Reading
60(1)
Chapter 4 Reasoning Methodologies in Clinical Decision Support Systems: A Literature Review 61(28)
Nora Shoaip
Shaker EI-Sappagh
Sherif Barakat
Mohammed Elmogy
1 Introduction
61(6)
2 Methods
67(1)
2.1 Research Questions
67(1)
2.2 Selection Criteria
67(1)
2.3 Search Strategy
68(1)
3 Literature Review and Results
68(15)
3.1 Paper Screening
69(2)
3.2 Selecting the Most Relevant Papers
71(1)
3.3 Extracting and Analyzing Concepts
72(10)
3.4 Current Challenges and Future Trends
82(1)
4 Conclusion
83(1)
References
84(5)
Chapter 5 Embedded Healthcare System for Day-to-Day Fitness, Chronic Kidney Disease, and Congestive Heart Failure 89(30)
Pradeep M. Patil
Durgaprasad K. Kamat
1 Ubiquitous Healthcare and Present
Chapter
90(1)
2 Introduction
90(2)
3 Frequency-Dependent Behavior of Body Composition
92(1)
4 Bioimpedance Analysis for Estimation of Day-to-Day Fitness and Chronic Diseases
93(5)
5 Measurement System for Body Composition Analysis Using Bioimpedance Principle
98(7)
5.1 Measurement Electrodes
99(1)
5.2 AFE4300 Body Composition Analyzer
99(6)
5.3 Statistical Analysis
105(1)
5.4 Validation of Developed Model
105(1)
6 Database Generation
105(1)
7 Predictive Regression Model for Day-to-Day Fitness
106(5)
8 Predictive Regression Model for CKD
111(2)
9 Predictive Regression Model for CHF
113(2)
10 Discussion
115(1)
11 Conclusion
115(1)
References
116(3)
Chapter 6 Comparison of Multiclass and Hierarchical CAC Design for Benign and Malignant Hepatic Tumors 119(28)
Nimisha Manth
Kriti
Jitendra Virmani
1 Introduction
120(3)
2 Materials and Methods
123(14)
2.1 Dataset Collection
123(1)
2.2 Data Set Description
123(1)
2.3 Data Collection Protocol
123(1)
2.4 ROIs Selection
124(1)
2.5 ROI Size Selection
125(2)
2.6 Proposed CAC System Design
127(1)
2.7 Feature Extraction Module
127(5)
2.8 Classification Module
132(5)
3 Results
137(4)
3.1 Experiment 1: To Evaluate the Potential of the Three-Class SSVM Classifier Design for the Characterization of Benign and Malignant FHTs
139(1)
3.2 Experiment 2: To Evaluate the Potential of SSVM-Based Hierarchical Classifier Design for Characterization Between Benign and Malignant FHTs
139(1)
3.3 Experiment 3: Performance Comparison of SSVM-Based Three-Class Classifier Design and SSVM-Based Hierarchical Classifier Design for Characterization of Benign and Malignant FHTs
140(1)
4 Discussion and Conclusion
141(3)
References
144(2)
Further Reading
146(1)
Chapter 7 Ontology Enhanced Fuzzy Clinical Decision Support System 147(32)
Nora Shoaip
Shaker EI-Sappagh
Sherif Barakat
Mohammed Elmogy
1 Introduction
147(5)
2 Problem Description
152(1)
3 Related Work
153(3)
4 The Combining of Ontology and Fuzzy Logic Frameworks
156(4)
5 System Architecture and Research Methodology
160(12)
5.1 Knowledge Acquisition
160(3)
5.2 Semantic Modeling
163(1)
5.3 The Fuzzy Modeling
164(6)
5.4 Knowledge Reasoning
170(2)
6 Conclusion
172(2)
References
174(3)
Further Reading
177(2)
Chapter 8 Improving the Prediction Accuracy of Heart Disease with Ensemble Learning and Majority Voting Rule 179(18)
Khalid Raza
1 Introduction
179(2)
2 Review of Related Works
181(2)
3 Ensemble Learning Systems
183(2)
3.1 Diversity
184(1)
3.2 Training Ensemble Members
184(1)
3.3 Combining Ensemble Members
184(1)
4 Materials and Methods
185(6)
4.1 Logistic Regression
185(3)
4.2 Multilayer Perceptron
188(1)
4.3 Naive Bayes
188(1)
4.4 Combining Classifiers Using Majority Vote Rule
189(1)
4.5 Performance Metrics
190(1)
5 Result and Discussion
191(2)
6 Conclusion and Future Directions
193(1)
References
193(3)
Further Reading
196(1)
Chapter 9 Machine Learning for Medical Diagnosis: A Neural Network Classifier Optimized Via the Directed Bee Colony Optimization Algorithm 197(20)
SaurabhKumar Agrawal
BhanuPratap Singh
Rajesh Kumar
Nilanjan Dey
1 Introduction
197(3)
2 Neural Network Dynamics
200(1)
3 Directed Bee Colony Optimization Algorithm
201(3)
4 Experimental Setup
204(1)
5 Result and Discussion
204(9)
6 Conclusion
213(1)
References
214(1)
Further Reading
215(2)
Chapter 10 A Genetic Algorithm-Based Metaheuristic Approach to Customize a Computer-Aided Classification System for Enhanced Screen Film Mammograms 217(44)
Heminder Kaur
Jitendra Virmani
Kriti
Shruti Thakur
1 Introduction
218(8)
2 Methodology for Designing a CAD System for Diagnosis of Abnormal Mammograms
226(20)
2.1 Image Data Set Description
228(1)
2.2 Enhancement Methods
229(8)
2.3 Selection of ROIs
237(4)
2.4 Feature Extraction: Gabor Wavelet Transform Features
241(3)
2.5 SVM Classifier
244(2)
3 Experimental Results
246(5)
3.1 Obtaining the Accuracies of Classification of Abnormal Mammograms After Enhancement With Alpha Trimmed Mean Filter
246(1)
3.2 Obtaining the Accuracies of Classification of Diagnosis of Abnormal Mammograms After Enhancement With Contrast Stretching
246(1)
3.3 Obtaining the Accuracies of Classification of Diagnosis of Abnormal Mammograms After Enhancement With Histogram Equalization
247(1)
3.4 Obtaining the Accuracies of Classification of Diagnosis of Abnormal Mammograms After Enhancement With CLAHE
247(1)
3.5 Obtaining the Accuracies of Classification of Diagnosis of Abnormal Mammograms After Enhancement With RMSHE
247(1)
3.6 Obtaining the Accuracies of Classification of Diagnosis of Abnormal Mammograms After Enhancement With Contra-Harmonic Mean
248(1)
3.7 Obtaining the Accuracies of Classification of Diagnosis of Abnormal Mammograms After Enhancement With Mean Filter
248(1)
3.8 Obtaining the Accuracies of Classification of Diagnosis of Abnormal Mammograms After Enhancement With Median Filter
248(1)
3.9 Obtaining the Accuracies of Classification of Diagnosis of Abnormal Mammograms After Enhancement With Hybrid Median Filter
249(1)
3.10 Obtaining the Accuracies of Classification of Diagnosis of Abnormal Mammograms After Morphological Enhancement
249(1)
3.11 Obtaining the Accuracies of Classification of Diagnosis of Abnormal Mammograms After Morphological Enhancement, Followed by Contrast Stretching
250(1)
3.12 Obtaining the Accuracies of Classification of Diagnosis of Abnormal Mammograms After Unsharp Masking
250(1)
3.13 Obtaining the Accuracies of Classification of Diagnosis of Abnormal Mammograms After UMCA
250(1)
3.14 Obtaining the Accuracies of Classification of Diagnosis of Abnormal Mammograms After Wavelet-Based Subband Filtering
251(1)
4 Comparison of Classification Performance of the Enhancement Methods
251(1)
5 Genetic Algorithm-Based Metaheuristic Approach to Customize a Computer-Aided Classification System for Enhanced Mammograms
252(2)
6 Conclusion
254(1)
7 Future Scope
254(1)
References
255(4)
Further Reading
259(2)
Chapter 11 Embedded Healthcare System Based on Bioimpedance Analysis for Identification and Classification of Skin Diseases in Indian Context 261(28)
Pradeep M. Patil
Durgaprasad K. Kamat
1 Introduction
262(1)
2 Need of Bioimpedance Measurement for Identification and Classification of Skin Diseases
263(3)
3 System Developed for the Measurement of Human Skin Impedance
266(3)
3.1 Skin Electrode
267(1)
3.2 Impedance Converter IC AD5933
267(1)
3.3 Microcontroller IC CY7C68013A
268(1)
3.4 Personal Computer
269(1)
4 Generation of a Database of Indian Skin Diseases
269(1)
5 Impedance Indices for Identification and Classification of Skin Diseases
270(2)
6 Identification of Skin Diseases
272(6)
6.1 Wilcoxon Signed Rank Test
277(1)
7 Measures of Classification of Skin Diseases
278(3)
7.1 Box and Whisker Plot of Impedance Indices
278(2)
7.2 Mean and Standard Deviation of Impedance Indices
280(1)
8 Classification of Skin Diseases Using Modular Fuzzy Hypersphere Neural Network
281(5)
9 Conclusion
286(1)
References
286(3)
Chapter 12 A Hybrid CAD System Design for Liver Diseases Using Clinical and Radiological Data 289(26)
Shrestha Bansal
Gaurav Chhabra
B. Sarat Chandra
Kriti
Jitendra Virmani
1 Introduction
289(2)
2 Methodology Adopted
291(19)
2.1 CAD System Design A
293(8)
2.2 CAD System Design B
301(6)
2.3 CAD System Design C: Hybrid CAD System
307(3)
3 Discussion
310(1)
4 Conclusion and Future Scope
311(1)
References
311(3)
Further Reading
314(1)
Chapter 13 Ontology-Based Electronic Health Record Semantic Interoperability: A Survey 315(38)
Ebtsam Adel
Shaker EI-Sappagh
Sherif Barakat
Mohammed Elmogy
1 Introduction
315(2)
2 EHR and Its Interoperability
317
2.1 Introduction and Definitions
317(2)
2.2 The Interoperability Benefits
319(1)
2.3 The Different Interoperability Levels
320(1)
2.4 EHR Semantic Interoperability Requirements
321
3 E-Health Standards and Interoperability
124(208)
4 Ontologies and Their Role in EHR
332(4)
5 Methods
336(10)
5.1 Research Questions
336(1)
5.2 Search Strategy
336(1)
5.3 Search Results
337(7)
5.4 Discussion
344(2)
6 The Challenges of EHR Semantic Interoperability
346(1)
7 Conclusion
347(1)
References
348(5)
Chapter 14 A Unified Fuzzy Ontology for Distributed Electronic Health Record Semantic Interoperability 353(44)
Ebtsam Adel
Shaker EI-Sappagh
Sherif Barakat
Mohammed Elmogy
1 Introduction
354(6)
1.1 EHR Clinical and Business Benefits and Outcomes
355(2)
1.2 EHR Semantic Interoperability Barriers and Obstacles
357(3)
2 Related Work
360(2)
3 Preliminaries
362(11)
3.1 Techniques and Approaches of EHR Semantic Interoperability
362(1)
3.2 EHR Standards
363(1)
3.3 Ontologies
363(4)
3.4 Terminologies
367(2)
3.5 Semantic Interoperability Frameworks
369(3)
3.6 Privacy and Security in EHR Systems
372(1)
4 Methodology
373(16)
4.1 The Proposed Framework
374(7)
4.2 A Prototype Problem Example
381(8)
4.3 A Comparison Study
389(1)
5 Conclusion
389(1)
References
389(6)
Further Reading
395(2)
Index 397
Nilanjan Dey (Senior Member, IEEE) received the B.Tech., M.Tech. in information technology from West Bengal Board of Technical University and Ph.D. degrees in electronics and telecommunication engineering from Jadavpur University, Kolkata, India, in 2005, 2011, and 2015, respectively. Currently, he is Associate Professor with the Techno International New Town, Kolkata and a visiting fellow of the University of Reading, UK. He has authored over 300 research articles in peer-reviewed journals and international conferences and 40 authored books. His research interests include medical imaging and machine learning. Moreover, he actively participates in program and organizing committees for prestigious international conferences, including World Conference on Smart Trends in Systems Security and Sustainability (WorldS4), International Congress on Information and Communication Technology (ICICT), International Conference on Information and Communications Technology for Sustainable Development (ICT4SD) etc.

He is also the Editor-in-Chief of International Journal of Ambient Computing and Intelligence, Associate Editor of IEEE Transactions on Technology and Society and series Co-Editor of Springer Tracts in Nature-Inspired Computing and Data-Intensive Research from Springer Nature and Advances in Ubiquitous Sensing Applications for Healthcare from Elsevier etc. Furthermore, he was an Editorial Board Member Complex & Intelligence Systems, Springer, Applied Soft Computing, Elsevier and he is an International Journal of Information Technology, Springer, International Journal of Information and Decision Sciences etc. He is a Fellow of IETE and member of IE, ISOC etc.

Amira S. Ashour is an Assistant Professor and Head of Electronics and Electrical Communications Engineering Department, Faculty of Engineering, Tanta University, Egypt. She is a member in the Research and Development Unit, Faculty of Engineering, Tanta University, Egypt. She received the B.Eng. degree in Electrical Engineering from Faculty of Engineering, Tanta University, Egypt in 1997, M.Sc. in Image Processing in 2001 and Ph.D. in Smart Antenna in 2005 from Faculty of Engineering, Tanta University, Egypt. Ashour has been the Vice Chair of Computer Engineering Department, Computers and Information Technology College, Taif University, KSA for one year from 2015. She has been the vice chair of CS department, CIT college, Taif University, KSA for 5 years. Her research interests are Smart antenna, Direction of arrival estimation, Targets tracking, Image processing, Medical imaging, Machine learning, Biomedical Systems, Pattern recognition, Image analysis, Computer vision, Computer-aided detection and diagnosis systems, Optimization, and Neutrosophic theory. She has 15 books and about 150 published journal papers. She is an Editor-in-Chief for the International Journal of Synthetic Emotions (IJSE), IGI Global, US. Simon Fong graduated from La Trobe University, Australia, with a 1st Class Honours BEng, Computer Systems degree and a PhD, Computer Science degree in 1993 and 1998 respectively. Simon is now working as an Associate Professor at the Computer and Information Science Department of the University of Macau. He is a co-founder of the Data Analytics and Collaborative Computing Research Group in the Faculty of Science and Technology. Prior to his academic career, Simon took up various managerial and technical posts, such as systems engineer, IT consultant and e-commerce director in Australia and Asia. Dr. Fong has published over 373 international conference and peer-reviewed journal papers, mostly in the areas of data mining, data stream mining, big data analytics, meta-heuristics optimization algorithms, and their applications. He serves on the editorial boards of the Journal of Network and Computer Applications of Elsevier, IEEE IT Professional Magazine, and various special issues of SCIE-indexed journals. Surekha Borra is currently a Professor in the Department of ECE, K. S. Institute of Technology, Bangalore, India. She earned her Doctorate in Image Processing from Jawaharlal Nehru Technological University, Hyderabad, India, in 2015. Her research interests are in the areas of Image and Video Analytics, Machine Learning, Biometrics and Remote Sensing. She has published 1 edited book, 8 book chapters and 22 research papers to her credit in refereed & indexed journals, and conferences at international and national levels. Her international recognition includes her professional memberships & services in refereed organizations, programme committees, editorial & review boards, wherein she has been a guest editor for 2 journals and reviewer for journals published by IEEE, IET, Elsevier, Taylor & Francis, Springer, IGI-Global etc,. She has received Woman Achiever's Award from The Institution of Engineers (India), for her prominent research and innovative contribution (s)., Woman Educator & Scholar Award for her contributions to teaching and scholarly activities, Young Woman Achiever Award for her contribution in Copyright Protection of Images.
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