Engineering and Technology for Healthcare

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 xiiiIntroduction xv1 Maximizing the Value of Engineering and Technology Research in Healthcare: Development-Focused Health Technology Assessment 1Janet Boutell Hawkins and Eleanor Grieve1.1 Introduction 11.2 What Is HTA? 31.3 What Is Development-Focused HTA? 41.4 Illustration of Features of Development-Focused HTA 51.4.1 Use-Focused HTA 61.4.2 Development-Focused HTA 61.5 Activities of Development-Focused HTA 71.6 Analytical Methods of Development-Focused HTA 91.6.1 Clinical Value Assessment 111.6.2 Economic Value Assessment 111.6.3 Evidence Generation 141.7 What Are the Challenges in the Development and Assessment of Medical Devices? 151.7.1 What Are Medical Devices? 151.7.2 Challenges Common to All medical Devices 161.7.2.1 Licensing and Regulation 161.7.2.2 Adoption 171.7.2.3 Evidence 181.7.3 Challenges Specific to Some Categories of Device 191.7.3.1 Learning Curve 191.7.3.2 Short Lifespan and Incremental Improvement 191.7.3.3 Workflow 191.7.3.4 Indirect Health Benefit 191.7.3.5 Behavioral and Other Contextual Factors 201.7.3.6 Budgetary Challenges 201.8 The Contribution of DF-HTA in the Development and Translation of Medical Devices 201.8.1 Case Study 1 - Identifying and Confirming Needs 211.8.2 Case Study 2 - What Difference Could This Device Make? 211.8.3 Case Study 3 - Which Research Project Has the Most Potential? 211.8.4 Case Study 4 - What Is the Required Performance to Deliver Clinical Utility? 211.8.5 Case Study 5 - What Are the Key Parameters for Evidence Generation? 221.9 Conclusion 22References 232 Contactless Radar Sensing for Health Monitoring 29Francesco Fioranelli and Julien Le Kernec2.1 Introduction: Healthcare Provision and Radar Technology 292.2 Radar and Radar Data Fundamentals 322.2.1 Principles of Radar Systems 322.2.2 Principles of Radar Signal Processing for Health Applications 352.2.3 Principles of Machine Learning Applied to Radar Data 382.2.4 Complementary Approaches: Passive Radar and Channel State Information Sensing 412.3 Radar Technology in Use for Health Care 422.3.1 Activities Recognition and Fall Detection 422.3.2 Gait Monitoring 462.3.3 Vital Signs and Sleep Monitoring 482.4 Conclusion and Outstanding Challenges 502.5 Future Trends 522.5.1 Paradigm Change in Radar Sensing 522.5.2 Multimodal Sensing 55References 553 Pervasive Sensing: Macro to Nanoscale 61Qammer H. Abbasi, Hasan T. Abbas, Muhammad Ali Imran and Akram Alomainy3.1 Introduction 613.2 The Anatomy of a Human Skin 643.3 Characterization of Human Tissue 653.4 Tissue Sample Preparation 703.5 Measurement Apparatus 703.6 Simulating the Human Skin 723.6.1 Human Body Channel Modelling 733.7 Networking and Communication Mechanisms for Body-Centric Wireless Nano-Networks 763.8 Concluding Remarks 78References 784 Biointegrated Implantable Brain Devices 81Rupam Das and Hadi Heidari4.1 Background 814.2 Neural Device Interfaces 834.3 Implant Tissue Biointegration 844.4 MRI Compatibility of the Neural Devices 874.5 Conclusion 90References 905 Machine Learning for Decision Making in Healthcare 95Ali Rizwan, Metin Ozturk, Najah Abu Ali, Ahmed Zoha, Qammer H. Abbasi and M. Ali Imran5.1 Introduction 955.2 Data Description 985.3 Proposed Methodology 995.3.1 Collection of the Data 995.3.2 Selection of the Window Size 1005.3.3 Extraction of the Features 1015.3.4 Selection of the Features 1015.3.5 Deployment of the Machine Learning Models 1025.3.6 Quantitative Assessment of the Models 1035.3.7 Parallel Processing 1045.4 Results 1055.5 Analysis and Discussion 1085.5.1 Postures 1085.5.2 Window Sizes 1095.5.3 Feature Combinations 1095.5.4 Machine Learning Algorithms 1115.6 Conclusions 113References 1136 Information Retrieval from Electronic Health Records 117Meshal Al-Qahtani, Stamos Katsigiannis and Naeem Ramzan6.1 Introduction 1176.2 Methodology 1186.2.1 Parallel LSI (PLSI) 1196.2.2 Distributed LSI (DLSI) 1216.3 Results and Analysis 1226.4 Conclusion 126References 1267 Energy Harvesting for Wearable and Portable Devices 129Rami Ghannam, You Hao, Yuchi Liu and Yidi Xiao7.1 Introduction 1297.2 Energy Harvesting Techniques 1307.2.1 Photovoltaics 1307.2.2 Piezoelectric Energy Harvesting 1347.2.3 Thermal Energy Harvesting 1377.2.3.1 Latest Trends 1397.2.4 RF Energy Harvesting 1417.3 Conclusions 145References 1468 Wireless Control for Life-Critical Actions 153Burak Kizilkaya, Bo Chang, Guodong Zhao and Muhammad Ali Imran8.1 Introduction 1538.2 Wireless Control for Healthcare 1558.3 Technical Requirements 1568.3.1 Ultra-Reliability 1568.3.2 Low Latency 1568.3.3 Security and Privacy 1578.3.4 Edge Artificial Intelligence 1578.4 Design Aspects 1578.4.1 Independent Design 1588.4.2 Co-Design 1598.5 Co-Design System Model 1598.5.1 Control Function 1598.5.2 Performance Evaluation Criterion 1618.5.2.1 Control Performance 1618.5.2.2 Communication Performance 1618.5.3 Effects of Different QoS 1628.5.4 Numerical Results 1638.6 Conclusions 165References 1659 Role of D2D Communications in Mobile Health Applications: Security Threats and Requirements 169Muhammad Usman, Marwa Qaraqe, Muhammad Rizwan Asghar and Imran Shafique Ansari9.1 Introduction 1699.2 D2D Scenarios for Mobile Health Applications 1709.3 D2D Security Requirements and Standardization 1719.3.1 Security Issues on Configuration 1719.3.1.1 Configuration of the ProSe Enabled UE 1719.3.2 Security Issues on Device Discovery 1729.3.2.1 Direct Request and Response Discovery 1729.3.2.2 Open Direct Discovery 1739.3.2.3 Restricted Direct Discovery 1739.3.2.4 Registration in Network-Based ProSe Discovery 1739.3.3 Security Issues on One-to-Many Communications 1749.3.3.1 One-to-many communications between UEs 1749.3.3.2 Key Distribution for Group Communications 1749.3.4 Security Issues on One-to-One Communication 1759.3.4.1 One-to-One ProSe Direct Communication 1759.3.4.2 One-to-One ProSe Direct Communication 1759.3.5 Security Issues on ProSe Relays 1759.3.5.1 Maintaining 3GPP Communication Security through Relay 1759.3.5.2 UE-Network Relay 1769.3.5.3 UE-to-UE Relay 1769.4 Existing Solutions 1769.4.1 Key Management 1769.4.2 Routing 1789.4.3 Social Trust and Social Ties 1789.4.4 Access Control 1809.4.5 Physical Layer Security 1809.4.6 Network Coding 1839.5 Conclusion 183References 18310 Automated Diagnosis of Skin Cancer for Healthcare: Highlights and Procedures 187Maram A. Wahba and Amira S. Ashour10.1 Introduction 18710.2 Framework of Computer-Aided Skin Cancer Classification Systems 18810.2.1 Image Acquisition 18810.2.2 Image Pre-Processing 18910.2.2.1 Color Contrast Enhancement 18910.2.2.2 Artifact Removal 19010.2.3 Image Segmentation 19110.2.3.1 Thresholding-Based Segmentation 19210.2.3.2 Edge-Based Segmentation 19210.2.3.3 Region-Based Segmentation 19310.2.3.4 Active Contours-Based Segmentation 19310.2.3.5 Artificial Intelligence-Based Segmentation 19410.2.4 Feature Extraction 19510.2.4.1 Color-based Features 19610.2.4.2 Dimensional Features 19610.2.4.3 Texture-Based Features 19610.2.4.4 Dermoscopic Rules and Methods 19710.2.5 Feature Selection 20010.2.6 Classification 20110.2.7 Classification Performance Evaluation 20210.2.8 Computer-Aided Diagnosis Systems in Dermoscopic Images 20310.3 Conclusion 205Acknowledgment 205References 205Conclusions 213Index 215