Innovation in healthcare is currently a "hot" topic. Innovation allows us to think differently, to take risks and to develop ideas that are far better than existing solutions. Currently, there is no single book that covers all topics related to microelectronics, sensors, data, system integration and healthcare technology assessment in one reference. This book aims to critically evaluate current state-of-the-art technologies and provide readers with insights into developing new solutions. With contributions from a fully international team of experts across electrical engineering and biomedical fields, the book discusses how advances in sensing technology, computer science, communications systems and proteomics/genomics are influencing healthcare technology today.

EDITED BY

MUHAMMAD ALI IMRAN, is Dean Glasgow College UESTC, Professor of Communication Systems and Head of Communications Sensing and Imaging group in the James Watt School of Engineering at the University of Glasgow, UK.

RAMI GHANNAM, is Lecturer (Assistant Professor) in Electronic Engineering and head of the Engineering Education Research Group in the James Watt School of Engineering at the University of Glasgow, UK.

QAMMER H. ABBASI, is Senior Lecturer (Associate Professor) and Deputy Head of Communications Sensing and Imaging group in the James Watt School of Engineering at the University of Glasgow, UK.

List of Contributors xiii

Introduction xv

1 Maximizing the Value of Engineering and Technology Research in Healthcare: Development-Focused Health Technology Assessment 1
Janet Boutell Hawkins and Eleanor Grieve

1.1 Introduction 1

1.2 What Is HTA? 3

1.3 What Is Development-Focused HTA? 4

1.4 Illustration of Features of Development-Focused HTA 5

1.4.1 Use-Focused HTA 6

1.4.2 Development-Focused HTA 6

1.5 Activities of Development-Focused HTA 7

1.6 Analytical Methods of Development-Focused HTA 9

1.6.1 Clinical Value Assessment 11

1.6.2 Economic Value Assessment 11

1.6.3 Evidence Generation 14

1.7 What Are the Challenges in the Development and Assessment of Medical Devices? 15

1.7.1 What Are Medical Devices? 15

1.7.2 Challenges Common to All medical Devices 16

1.7.2.1 Licensing and Regulation 16

1.7.2.2 Adoption 17

1.7.2.3 Evidence 18

1.7.3 Challenges Specific to Some Categories of Device 19

1.7.3.1 Learning Curve 19

1.7.3.2 Short Lifespan and Incremental Improvement 19

1.7.3.3 Workflow 19

1.7.3.4 Indirect Health Benefit 19

1.7.3.5 Behavioral and Other Contextual Factors 20

1.7.3.6 Budgetary Challenges 20

1.8 The Contribution of DF-HTA in the Development and Translation of Medical Devices 20

1.8.1 Case Study 1 - Identifying and Confirming Needs 21

1.8.2 Case Study 2 - What Difference Could This Device Make? 21

1.8.3 Case Study 3 - Which Research Project Has the Most Potential? 21

1.8.4 Case Study 4 - What Is the Required Performance to Deliver Clinical Utility? 21

1.8.5 Case Study 5 - What Are the Key Parameters for Evidence Generation? 22

1.9 Conclusion 22

References 23

2 Contactless Radar Sensing for Health Monitoring 29
Francesco Fioranelli and Julien Le Kernec

2.1 Introduction: Healthcare Provision and Radar Technology 29

2.2 Radar and Radar Data Fundamentals 32

2.2.1 Principles of Radar Systems 32

2.2.2 Principles of Radar Signal Processing for Health Applications 35

2.2.3 Principles of Machine Learning Applied to Radar Data 38

2.2.4 Complementary Approaches: Passive Radar and Channel State Information Sensing 41

2.3 Radar Technology in Use for Health Care 42

2.3.1 Activities Recognition and Fall Detection 42

2.3.2 Gait Monitoring 46

2.3.3 Vital Signs and Sleep Monitoring 48

2.4 Conclusion and Outstanding Challenges 50

2.5 Future Trends 52

2.5.1 Paradigm Change in Radar Sensing 52

2.5.2 Multimodal Sensing 55

References 55

3 Pervasive Sensing: Macro to Nanoscale 61
Qammer H. Abbasi, Hasan T. Abbas, Muhammad Ali Imran and Akram Alomainy

3.1 Introduction 61

3.2 The Anatomy of a Human Skin 64

3.3 Characterization of Human Tissue 65

3.4 Tissue Sample Preparation 70

3.5 Measurement Apparatus 70

3.6 Simulating the Human Skin 72

3.6.1 Human Body Channel Modelling 73

3.7 Networking and Communication Mechanisms for Body-Centric Wireless Nano-Networks 76

3.8 Concluding Remarks 78

References 78

4 Biointegrated Implantable Brain Devices 81
Rupam Das and Hadi Heidari

4.1 Background 81

4.2 Neural Device Interfaces 83

4.3 Implant Tissue Biointegration 84

4.4 MRI Compatibility of the Neural Devices 87

4.5 Conclusion 90

References 90

5 Machine Learning for Decision Making in Healthcare 95
Ali Rizwan, Metin Ozturk, Najah Abu Ali, Ahmed Zoha, Qammer H. Abbasi and M. Ali Imran

5.1 Introduction 95

5.2 Data Description 98

5.3 Proposed Methodology 99

5.3.1 Collection of the Data 99

5.3.2 Selection of the Window Size 100

5.3.3 Extraction of the Features 101

5.3.4 Selection of the Features 101

5.3.5 Deployment of the Machine Learning Models 102

5.3.6 Quantitative Assessment of the Models 103

5.3.7 Parallel Processing 104

5.4 Results 105

5.5 Analysis and Discussion 108

5.5.1 Postures 108

5.5.2 Window Sizes 109

5.5.3 Feature Combinations 109

5.5.4 Machine Learning Algorithms 111

5.6 Conclusions 113

References 113

6 Information Retrieval from Electronic Health Records 117
Meshal Al-Qahtani, Stamos Katsigiannis and Naeem Ramzan

6.1 Introduction 117

6.2 Methodology 118

6.2.1 Parallel LSI (PLSI) 119

6.2.2 Distributed LSI (DLSI) 121

6.3 Results and Analysis 122

6.4 Conclusion 126

References 126

7 Energy Harvesting for Wearable and Portable Devices 129
Rami Ghannam, You Hao, Yuchi Liu and Yidi Xiao

7.1 Introduction 129

7.2 Energy Harvesting Techniques 130

7.2.1 Photovoltaics 130

7.2.2 Piezoelectric Energy Harvesting 134

7.2.3 Thermal Energy Harvesting 137

7.2.3.1 Latest Trends 139

7.2.4 RF Energy Harvesting 141

7.3 Conclusions 145

References 146

8 Wireless Control for Life-Critical Actions 153
Burak Kizilkaya, Bo Chang, Guodong Zhao and Muhammad Ali Imran

8.1 Introduction 153

8.2 Wireless Control for Healthcare 155

8.3 Technical Requirements 156

8.3.1 Ultra-Reliability 156

8.3.2 Low Latency 156

8.3.3 Security and Privacy 157

8.3.4 Edge Artificial Intelligence 157

8.4 Design Aspects 157

8.4.1 Independent Design 158

8.4.2 Co-Design 159

8.5 Co-Design System Model 159

8.5.1 Control Function 159

8.5.2 Performance Evaluation Criterion 161

8.5.2.1 Control Performance 161

8.5.2.2 Communication Performance 161

8.5.3 Effects of Different QoS 162

8.5.4 Numerical Results 163

8.6 Conclusions 165

References 165

9 Role of D2D Communications in Mobile Health Applications: Security Threats and Requirements 169
Muhammad Usman, Marwa Qaraqe, Muhammad Rizwan Asghar and Imran Shafique Ansari

9.1 Introduction 169

9.2 D2D Scenarios for Mobile Health Applications 170

9.3 D2D Security Requirements and Standardization 171

9.3.1 Security Issues on Configuration 171

9.3.1.1 Configuration of the ProSe Enabled UE 171

9.3.2 Security Issues on Device Discovery 172

9.3.2.1 Direct Request and Response Discovery 172

9.3.2.2 Open Direct Discovery 173

9.3.2.3 Restricted Direct Discovery 173

9.3.2.4 Registration in Network-Based ProSe Discovery 173

9.3.3 Security Issues on One-to-Many Communications 174

9.3.3.1 One-to-many communications between UEs 174

9.3.3.2 Key Distribution for Group Communications 174

9.3.4 Security Issues on One-to-One Communication 175

9.3.4.1 One-to-One ProSe Direct Communication 175

9.3.4.2 One-to-One ProSe Direct Communication 175

9.3.5 Security Issues on ProSe Relays 175

9.3.5.1 Maintaining 3GPP Communication Security through Relay 175

9.3.5.2 UE-Network Relay 176

9.3.5.3 UE-to-UE Relay 176

9.4 Existing Solutions 176

9.4.1 Key Management 176

9.4.2 Routing 178

9.4.3 Social Trust and Social Ties 178

9.4.4 Access Control 180

9.4.5 Physical Layer Security 180

9.4.6 Network Coding 183

9.5 Conclusion 183

References 183

10 Automated Diagnosis of Skin Cancer for Healthcare: Highlights and Procedures 187
Maram A. Wahba and Amira S. Ashour

10.1 Introduction 187

10.2 Framework of Computer-Aided Skin Cancer Classification Systems 188

10.2.1 Image Acquisition 188

10.2.2 Image Pre-Processing 189

10.2.2.1 Color Contrast Enhancement 189

10.2.2.2 Artifact Removal 190

10.2.3 Image Segmentation 191

10.2.3.1 Thresholding-Based Segmentation 192

10.2.3.2 Edge-Based Segmentation 192

10.2.3.3 Region-Based Segmentation 193

10.2.3.4 Active Contours-Based Segmentation 193

10.2.3.5 Artificial Intelligence-Based Segmentation 194

10.2.4 Feature Extraction 195

10.2.4.1 Color-based Features 196

10.2.4.2 Dimensional Features 196

10.2.4.3 Texture-Based Features 196

10.2.4.4 Dermoscopic Rules and Methods 197

10.2.5 Feature Selection 200

10.2.6 Classification 201

10.2.7 Classification Performance Evaluation 202

10.2.8 Computer-Aided Diagnosis Systems in Dermoscopic Images 203

10.3 Conclusion 205

Acknowledgment 205

References 205

Conclusions 213

Index 215