Engineering and Technology for Healthcare

Name: Engineering and Technology for Healthcare
Brand: Wiley
Price: 108.99 EUR
Availability: OnlineOnly

Muhammad Ali Imran Rami Ghannam Qammer H. Abbasi(Autor*in)

Wiley (Verlag)

1. Auflage

Erschienen am 24. November 2020

240 Seiten

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978-1-119-64422-4 (ISBN)

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Inhalt

Cover
Title Page
Copyright
Contents
List of Contributors
Introduction
Chapter 1 Maximizing the Value of Engineering and Technology Research in Healthcare: Development-Focused Health Technology Assessment
1.1 Introduction
1.2 What Is HTA?
1.3 What Is Development-Focused HTA?
1.4 Illustration of Features of Development-Focused HTA
1.4.1 Use-Focused HTA
1.4.2 Development-Focused HTA
1.5 Activities of Development-Focused HTA
1.6 Analytical Methods of Development-Focused HTA
1.6.1 Clinical Value Assessment
1.6.2 Economic Value Assessment
1.6.3 Evidence Generation
1.7 What Are the Challenges in the Development and Assessment of Medical Devices?
1.7.1 What Are Medical Devices?
1.7.2 Challenges Common to All medical Devices
1.7.2.1 Licensing and Regulation
1.7.2.2 Adoption
1.7.2.3 Evidence
1.7.3 Challenges Specific to Some Categories of Device
1.7.3.1 Learning Curve
1.7.3.2 Short Lifespan and Incremental Improvement
1.7.3.3 Workflow
1.7.3.4 Indirect Health Benefit
1.7.3.5 Behavioral and Other Contextual Factors
1.7.3.6 Budgetary Challenges
1.8 The Contribution of DF-HTA in the Development and Translation of Medical Devices
1.8.1 Case Study 1 - Identifying and Confirming Needs
1.8.2 Case Study 2 - What Difference Could This Device Make?
1.8.3 Case Study 3 - Which Research Project Has the Most Potential?
1.8.4 Case Study 4 - What Is the Required Performance to Deliver Clinical Utility?
1.8.5 Case Study 5 - What Are the Key Parameters for Evidence Generation?
1.9 Conclusion
References
Chapter 2 Contactless Radar Sensing for Health Monitoring
2.1 Introduction: Healthcare Provision and Radar Technology
2.2 Radar and Radar Data Fundamentals
2.2.1 Principles of Radar Systems
2.2.2 Principles of Radar Signal Processing for Health Applications
2.2.3 Principles of Machine Learning Applied to Radar Data
2.2.4 Complementary Approaches: Passive Radar and Channel State Information Sensing
2.3 Radar Technology in Use for Health Care
2.3.1 Activities Recognition and Fall Detection
2.3.2 Gait Monitoring
2.3.3 Vital Signs and Sleep Monitoring
2.4 Conclusion and Outstanding Challenges
2.5 Future Trends
2.5.1 Paradigm Change in Radar Sensing
2.5.2 Multimodal Sensing
References
Chapter 3 Pervasive Sensing: Macro to Nanoscale
3.1 Introduction
3.2 The Anatomy of a Human Skin
3.3 Characterization of Human Tissue
3.4 Tissue Sample Preparation
3.5 Measurement Apparatus
3.6 Simulating the Human Skin
3.6.1 Human Body Channel Modelling
3.7 Networking and Communication Mechanisms for Body-Centric Wireless Nano-Networks
3.8 Concluding Remarks
References
Chapter 4 Biointegrated Implantable Brain Devices
4.1 Background
4.2 Neural Device Interfaces
4.3 Implant Tissue Biointegration
4.4 MRI Compatibility of the Neural Devices
4.5 Conclusion
References
Chapter 5 Machine Learning for Decision Making in Healthcare
5.1 Introduction
5.2 Data Description
5.3 Proposed Methodology
5.3.1 Collection of the Data
5.3.2 Selection of the Window Size
5.3.3 Extraction of the Features
5.3.4 Selection of the Features
5.3.5 Deployment of the Machine Learning Models
5.3.6 Quantitative Assessment of the Models
5.3.7 Parallel Processing
5.4 Results
5.5 Analysis and Discussion
5.5.1 Postures
5.5.2 Window Sizes
5.5.3 Feature Combinations
5.5.4 Machine Learning Algorithms
5.6 Conclusions
References
Chapter 6 Information Retrieval from Electronic Health Records
6.1 Introduction
6.2 Methodology
6.2.1 Parallel LSI (PLSI)
6.2.2 Distributed LSI (DLSI)
6.3 Results and Analysis
6.4 Conclusion
References
Chapter 7 Energy Harvesting for Wearable and Portable Devices
7.1 Introduction
7.2 Energy Harvesting Techniques
7.2.1 Photovoltaics
7.2.2 Piezoelectric Energy Harvesting
7.2.3 Thermal Energy Harvesting
7.2.3.1 Latest Trends
7.2.4 RF Energy Harvesting
7.3 Conclusions
References
Chapter 8 Wireless Control for Life-Critical Actions
8.1 Introduction
8.2 Wireless Control for Healthcare
8.3 Technical Requirements
8.3.1 Ultra-Reliability
8.3.2 Low Latency
8.3.3 Security and Privacy
8.3.4 Edge Artificial Intelligence
8.4 Design Aspects
8.4.1 Independent Design
8.4.2 Co-Design
8.5 Co-Design System Model
8.5.1 Control Function
8.5.2 Performance Evaluation Criterion
8.5.2.1 Control Performance
8.5.2.2 Communication Performance
8.5.3 Effects of Different QoS
8.5.4 Numerical Results
8.6 Conclusions
References
Chapter 9 Role of D2D Communications in Mobile Health Applications: Security Threats and Requirements
9.1 Introduction
9.2 D2D Scenarios for Mobile Health Applications
9.3 D2D Security Requirements and Standardization
9.3.1 Security Issues on Configuration
9.3.1.1 Configuration of the ProSe Enabled UE
9.3.2 Security Issues on Device Discovery
9.3.2.1 Direct Request and Response Discovery
9.3.2.2 Open Direct Discovery
9.3.2.3 Restricted Direct Discovery
9.3.2.4 Registration in Network-Based ProSe Discovery
9.3.3 Security Issues on One-to-Many Communications
9.3.3.1 One-to-many communications between UEs
9.3.3.2 Key Distribution for Group Communications
9.3.4 Security Issues on One-to-One Communication
9.3.4.1 One-to-One ProSe Direct Communication
9.3.4.2 One-to-One ProSe Direct Communication
9.3.5 Security Issues on ProSe Relays
9.3.5.1 Maintaining 3GPP Communication Security through Relay
9.3.5.2 UE-Network Relay
9.3.5.3 UE-to-UE Relay
9.4 Existing Solutions
9.4.1 Key Management
9.4.2 Routing
9.4.3 Social Trust and Social Ties
9.4.4 Access Control
9.4.5 Physical Layer Security
9.4.6 Network Coding
9.5 Conclusion
References
Chapter 10 Automated Diagnosis of Skin Cancer for Healthcare: Highlights and Procedures
10.1 Introduction
10.2 Framework of Computer-Aided Skin Cancer Classification Systems
10.2.1 Image Acquisition
10.2.2 Image Pre-Processing
10.2.2.1 Color Contrast Enhancement
10.2.2.2 Artifact Removal
10.2.3 Image Segmentation
10.2.3.1 Thresholding-Based Segmentation
10.2.3.2 Edge-Based Segmentation
10.2.3.3 Region-Based Segmentation
10.2.3.4 Active Contours-Based Segmentation
10.2.3.5 Artificial Intelligence-Based Segmentation
10.2.4 Feature Extraction
10.2.4.1 Color-based Features
10.2.4.2 Dimensional Features
10.2.4.3 Texture-Based Features
10.2.4.4 Dermoscopic Rules and Methods
10.2.5 Feature Selection
10.2.6 Classification
10.2.7 Classification Performance Evaluation
10.2.8 Computer-Aided Diagnosis Systems in Dermoscopic Images
10.3 Conclusion
Acknowledgment
References
Conclusions
Index
EULA

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