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.

Contributors

1.1.         Introduction       ix

1.2.         Bibliography       xv

2. Maximising the value of engineering and technology research in healthcare: development-focused health technology assessment

2.1.         Introduction      

2.2.         What is HTA?

2.3.         What is development-focused HTA?

2.4.         Illustration of features of development-focused HTA?

2.4.1.     Use-focused HTA?

2.4.2.     Development-focused HTA?

2.5.         Activities of development-focused HTA?

2.6.         Analytical methods of development-focused HTA             

2.6.1.     Clinical value assessment

2.6.2.     Economic value assessment

2.6.3.     Evidence generation

2.7.         What are the challenges in the development and assessment of medical devices?

2.7.1.     What are the medical devices?

2.7.2.     Challenges common to all medical devices

2.7.2.1. Licencing and regulation

2.7.2.2. Adoption

2.7.2.3. Evidence

2.7.3.     Challenges specific to some categories of device

2.7.3.1. Learning curve

2.7.3.2. Short lifespan and incremental improvement

2.7.3.3. Workflow

2.7.3.4. Indirect health benefit

2.7.3.5. Behavioural and other contextual factors

2.7.3.6. Budgetary challenge

2.8.         The contribution of DF-HTA in the development and translation of medical devices

2.8.1.     Case study 1 - Identifying and confirming needs

2.8.2.     Case study 2 - What difference could this device make?

2.8.3.     Case  study  3  -  Which  research  project has the most potential?

2.8.4.     Case study 4 - What is the required performance to deliver clinical utility?

2.8.5.     Case study 5 - What are the key param-eters for evidence generation?

2.9.         Conclusion

3. Contactless Radar Sensing for health monitoring

3.3.1. Introduction: healthcare provision and radar technology

3.3.2. Radar and Radar Data Fundamentals

3.3.2.1. Principles of radar systems

3.3.2.2. Principles of radar signal processing for health applications

3.3.3. Principles of machine learning applied to radar data

3.3.4. Complementary approaches: passive radar and channel state information sensing

3.4. Radar technology in use for healthcare

3.4.1. Activities recognition and fall detection

3.4.2.Gait monitoring

3.4.3. Vital signs and sleep monitoring

3.5. Conclusion and outstanding challenges

3.6. Future Trends

4. Pervasive Sensing: Macro to Nanoscale

4.1 Introduction

4.2. The anatomy of a human skin

4.3. Chracteristic of human tissue

4.4 Tissue Sample Preparation

4.5. Measurement Apparatus

4.6. Simulating the human skin

4.6.1. Human body channel modelling

4.7. Networking and Communication Mech-anisms for Body-Centric WirelessNano-Networks

4.8. Concluding Remarks

5. Bio integrated Implantable Brain Devices

5.1. Background

5.2 Neural Device Interfaces

5.3. Implant Tissue Biointegration

5.4. MRI Compatibility of the NeuralDevices

5.5. Conclusion

6. Machine Learning for Decision Making in Healthcare

6.1. Introduction

6.2. Data Description

6.3. Proposed Methodology

6.3.1. Data collection

6.3.2. Window size selection

6.3.3. Feature Extraction

6.3.4. Feature Selection

6.3.5. Implementation of Machine learning Models

6.3.6. Model Evaluation

6.4. Results

6.5. Analysis and Discussion

6.5.1. Impact of Postures

6.5.2. Impact of Windows Size

6.5.2. Impact of Feature combination

6.5.3. Impact of Machine Learning algorithms

6.6. Conclusion

7. Information Retrieval from Electronic Health Records

7.1. Introduction

7.2. Methodology

7.2.1. Parallel LSI (PLSI)

7.2.2. Distributed LSI (DLSI)

7.3. Results and Analysis

7.4 Conclusion

8. Energy Harvesting for Wearable and Portable Devices

8.1. Introduction

8.2. Energy Harvesting Techniques

8.2.1. Photovoltaics

8.2.2. Piezoelectric Energy Harvesting

8.2.3. Thermal Energy Harvesting

8.2.3.1. Last Trends

8.2.4. RF Energy Harvesting

8.3. Conclusion

9. Wireless control for life-critical actions

9.1. Introduction

9.2. Wireless Control for Healthcare

9.3. Technical Requirements

9.3.1. Ultra-Reliability

9.3.2. Low Latency

9.3.3. Security and Privacy

9.3.4. Edge Artificial Intelligence

9.4. Design Aspects

9.4.1. Independent Design

9.4.2. Co-Design

9.5. Co-Design System Model

9.5.1. Control Fusion

9.5.2. Performance Evaluation Criterion

9.5.2.1. Control Performance

9.5.2.2. Communication Performance

9.5.3. Effects of Different QoS

9.5.4. Simulation Results

9.6. Conclusion

10. ROLE OF D2D COMMUNICATIONS INMOBILE HEALTH APPLICATIONS: SECURITY THREATS AND REQUIREMENTS

10.1. Introduction

10.2. D2D Scenarios for Mobile Health Applications

10.3. D2D Security Requirements and Standardisation

10.3.1.  Security Issues on Configuration

10.3.1.1. Configuration  of  the  ProSe  enabled  UE

10.3.1.2. Security Issues on Device Discovery

10.3.1.2.1. Direct Request and Response Discovery

10.3.1.2.2. Open Direct Discovery

10.3.1.2.3. Restricted Directory

10.3.1.2.4. Registration in network-based ProSe Discovery

10.3.2. Security Issues on One-to-Many Communications

10.3.2.1. One-to-many communications between UEs

10.3.2.2. Key distribution for group communications

10.3.3. Security Issues on One-to-One Communication

10.3.3.1. One-to-one ProSe direct communication

10.3.3.2. One-to-one  ProSe  direct  communication

10.3.4. Security Issues on ProSe Relays

10.3.4.1. Maintaining 3GPP communication security through relay

10.3.4.2. UE-Network relay

10.3.4.3. UE-to-UE  relay

10.4.  Existing Solutions

10.4.1. Key Management

10.4.2. Routing

10.4.3. Social Trust and social ties

10.4.4. Access Control

10.4.5. Physical Layer Security

10.4.6. Network Coding

10.5. Conclusion

11. Automated diagnosis of skin cancer for healthcare: Highlights and Procedures

11.1. Introduction

11.2. Framework of Computer-aided Skin Cancer Classification Systems

11.2.1. Image Acquisition

11.2.2. Image Pre-processing

11.2.2.1. Color Contrast Enhancement

11.2.2.2. Artificial Removal

11.2.3. Image Segmentation

11.2.3.1. Thresholding-based Segmentation

11.2.3.2. Edge-based Segmentation

11.2.3.3. Region-based Segmentation

11.2.3.4. Active contours-based Segmentation

11.2.3.5. Artificial Intelligence-based Segmentation

11.2.4. Feature Extraction

11.2.4.1. Color-based Features

11.2.4.2. Dimensional Features

11.2.4.3. Textual-based Features

11.2.4.4. Dermoscopic Rules and Methods

11.2.4.4.1. ABCD Rule

11.2.4.4.2 Menzies Method

11.2.4.4.3 7-Point Checklist

11.2.5. Feature Selection

11.2.6. Classification

11.2.7. Classification Performance Evaluation

11.3. Conclusion

12. Conclusion

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