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E-raamat: Engineering and Technology for Healthcare

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  • Formaat: EPUB+DRM
  • Sari: IEEE Press
  • Ilmumisaeg: 25-Nov-2020
  • Kirjastus: Wiley-IEEE Press
  • Keel: eng
  • ISBN-13: 9781119644286
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Engineering and Technology for HealthcareSuurem pilt
  • Formaat: EPUB+DRM
  • Sari: IEEE Press
  • Ilmumisaeg: 25-Nov-2020
  • Kirjastus: Wiley-IEEE Press
  • Keel: eng
  • ISBN-13: 9781119644286
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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.
List of Contributors
xiii
Introduction xv
1 Maximizing the Value of Engineering and Technology Research in Healthcare: Development-Focused Health Technology Assessment
1(28)
Janet Boutell Hawkins
Eleanor Grieve
1.1 Introduction
1(2)
1.2 What Is HTA?
3(1)
1.3 What Is Development-Focused HTA?
4(1)
1.4 Illustration of Features of Development-Focused HTA
5(2)
1.4.1 Use-Focused HTA
6(1)
1.4.2 Development-Focused HTA
6(1)
1.5 Activities of Development-Focused HTA
7(2)
1.6 Analytical Methods of Development-Focused HTA
9(6)
1.6.1 Clinical Value Assessment
11(1)
1.6.2 Economic Value Assessment
11(3)
1.6.3 Evidence Generation
14(1)
1.7 What Are the Challenges in the Development and Assessment of Medical Devices?
15(5)
1.7.1 What Are Medical Devices?
15(1)
1.7.2 Challenges Common to All medical Devices
16(1)
1.7.2.1 Licensing and Regulation
16(1)
1.7.2.2 Adoption
17(1)
1.7.2.3 Evidence
18(1)
1.7.3 Challenges Specific to Some Categories of Device
19(1)
1.7.3.1 Learning Curve
19(1)
1.7.3.2 Short Lifespan and Incremental Improvement
19(1)
1.7.3.3 Workflow
19(1)
1.7.3.4 Indirect Health Benefit
19(1)
1.7.3.5 Behavioral and Other Contextual Factors
20(1)
1.7.3.6 Budgetary Challenges
20(1)
1.8 The Contribution of DF-HTA in the Development and Translation of Medical Devices
20(2)
1.8.1 Case Study 1 -- Identifying and Confirming Needs
21(1)
1.8.2 Case Study 2 -- What Difference Could This Device Make?
21(1)
1.8.3 Case Study 3 -- Which Research Project Has the Most Potential?
21(1)
1.8.4 Case Study 4 -- What Is the Required Performance to Deliver Clinical Utility?
21(1)
1.8.5 Case Study 5 -- What Are the Key Parameters for Evidence Generation?
22(1)
1.9 Conclusion
22(7)
References
23(6)
2 Contactless Radar Sensing for Health Monitoring
29(32)
Francesco Fioranelli
Julien Le Kernec
2.1 Introduction: Healthcare Provision and Radar Technology
29(3)
2.2 Radar and Radar Data Fundamentals
32(10)
2.2.1 Principles of Radar Systems
32(3)
2.2.2 Principles of Radar Signal Processing for Health Applications
35(3)
2.2.3 Principles of Machine Learning Applied to Radar Data
38(3)
2.2.4 Complementary Approaches: Passive Radar and Channel State Information Sensing
41(1)
2.3 Radar Technology in Use for Health Care
42(8)
2.3.1 Activities Recognition and Fall Detection
42(4)
2.3.2 Gait Monitoring
46(2)
2.3.3 Vital Signs and Sleep Monitoring
48(2)
2.4 Conclusion and Outstanding Challenges
50(2)
2.5 Future Trends
52(9)
2.5.1 Paradigm Change in Radar Sensing
52(3)
2.5.2 Multimodal Sensing
55(1)
References
55(6)
3 Pervasive Sensing: Macro to Nanoscale
61(20)
Qammer H. Abbasi
Hasan T. Abbas
Muhammad Ali Imran
Akram Alomainy
3.1 Introduction
61(3)
3.2 The Anatomy of a Human Skin
64(1)
3.3 Characterization of Human Tissue
65(5)
3.4 Tissue Sample Preparation
70(1)
3.5 Measurement Apparatus
70(2)
3.6 Simulating the Human Skin
72(4)
3.6.1 Human Body Channel Modelling
73(3)
3.7 Networking and Communication Mechanisms for Body-Centric Wireless Nano-Networks
76(2)
3.8 Concluding Remarks
78(3)
References
78(3)
4 Biointegrated Implantable Brain Devices
81(14)
Rupam Das
Hadi Heidari
4.1 Background
81(2)
4.2 Neural Device Interfaces
83(1)
4.3 Implant Tissue Biointegration
84(3)
4.4 MRI Compatibility of the Neural Devices
87(3)
4.5 Conclusion
90(5)
References
90(5)
5 Machine Learning for Decision Making in Healthcare
95(22)
Ali Rizwan
Metin Ozturk
Najah Abu Ali
Ahmed Zoha
Qammer H. Abbasi
M. Ali Imran
5.1 Introduction
95(3)
5.2 Data Description
98(1)
5.3 Proposed Methodology
99(6)
5.3.1 Collection of the Data
99(1)
5.3.2 Selection of the Window Size
100(1)
5.3.3 Extraction of the Features
101(1)
5.3.4 Selection of the Features
101(1)
5.3.5 Deployment of the Machine Learning Models
102(1)
5.3.6 Quantitative Assessment of the Models
103(1)
5.3.7 Parallel Processing
104(1)
5.4 Results
105(3)
5.5 Analysis and Discussion
108(5)
5.5.1 Postures
108(1)
5.5.2 Window Sizes
109(1)
5.5.3 Feature Combinations
109(2)
5.5.4 Machine Learning Algorithms
111(2)
5.6 Conclusions
113(4)
References
113(4)
6 Information Retrieval from Electronic Health Records
117(12)
Meshal Al-Qahtani
Stamos Katsigiannis
Naeem Ramzan
6.1 Introduction
117(1)
6.2 Methodology
118(4)
6.2.1 Parallel LSI (PLSI)
119(2)
6.2.2 Distributed LSI (DLSI)
121(1)
6.3 Results and Analysis
122(4)
6.4 Conclusion
126(3)
References
126(3)
7 Energy Harvesting for Wearable and Portable Devices
129(24)
Rami Ghannam
You Hao
Yuchi Liu
Yidi Xiao
7.1 Introduction
129(1)
7.2 Energy Harvesting Techniques
130(15)
7.2.1 Photovoltaics
130(4)
7.2.2 Piezoelectric Energy Harvesting
134(3)
7.2.3 Thermal Energy Harvesting
137(2)
7.2.3.1 Latest Trends
139(2)
7.2.4 RF Energy Harvesting
141(4)
7.3 Conclusions
145(8)
References
146(7)
8 Wireless Control for Life-Critical Actions
153(16)
Burak Kizilkaya
Bo Chang
Guodong Zhao
Muhammad Ali Imran
8.1 Introduction
153(2)
8.2 Wireless Control for Healthcare
155(1)
8.3 Technical Requirements
156(1)
8.3.1 Ultra-Reliability
156(1)
8.3.2 Low Latency
156(1)
8.3.3 Security and Privacy
157(1)
8.3.4 Edge Artificial Intelligence
157(1)
8.4 Design Aspects
157(2)
8.4.1 Independent Design
158(1)
8.4.2 Co-Design
159(1)
8.5 Co-Design System Model
159(6)
8.5.1 Control Function
159(2)
8.5.2 Performance Evaluation Criterion
161(1)
8.5.2.1 Control Performance
161(1)
8.5.2.2 Communication Performance
161(1)
8.5.3 Effects of Different QoS
162(1)
8.5.4 Numerical Results
163(2)
8.6 Conclusions
165(4)
References
165(4)
9 Role of D2D Communications in Mobile Health Applications: Security Threats and Requirements
169(18)
Muhammad Usman
Marwa Qaraqe
Muhammad Rizwan Asghar
Imran Shafique Ansari
9.1 Introduction
169(1)
9.2 D2D Scenarios for Mobile Health Applications
170(1)
9.3 D2D Security Requirements and Standardization
171(5)
9.3.1 Security Issues on Configuration
171(1)
9.3.1.1 Configuration oftheProSe Enabled UE
171(1)
9.3.2 Security Issues on Device Discovery
172(1)
9.3.2.1 Direct Request and Response Discovery
172(1)
9.3.2.2 Open Direct Discovery
173(1)
9.3.2.3 Restricted Direct Discovery
173(1)
9.3.2.4 Registration in Network-Based ProSe Discovery
173(1)
9.3.3 Security Issues on One-to-Many Communications
174(1)
9.3.3.1 One-to-many communications between UEs
174(1)
9.3.3.2 Key Distribution for Group Communications
174(1)
9.3.4 Security Issues on One-to-One Communication
175(1)
9.3.4.1 One-to-One ProSe Direct Communication
175(1)
9.3.4.2 One-to-One ProSe Direct Communication
175(1)
9.3.5 Security Issues on ProSe Relays
175(1)
9.3.5.1 Maintaining 3GPP Communication Security through Relay
175(1)
9.3.5.2 UE-Network Relay
176(1)
9.3.5.3 UE-to-UE Relay
176(1)
9.4 Existing Solutions
176(7)
9.4.1 Key Management
176(2)
9.4.2 Routing
178(1)
9.4.3 Social Trust and Social Ties
178(2)
9.4.4 Access Control
180(1)
9.4.5 Physical Layer Security
180(3)
9.4.6 Network Coding
183(1)
9.5 Conclusion
183(4)
References
183(4)
10 Automated Diagnosis of Skin Cancer for Healthcare: Highlights and Procedures
187(26)
Maram A. Wahba
Amira S. Ashour
10.1 Introduction
187(1)
10.2 Framework of Computer-Aided Skin Cancer Classification Systems
188(17)
10.2.1 Image Acquisition
188(1)
10.2.2 Image Pre-Processing
189(1)
10.2.2.1 Color Contrast Enhancement
189(1)
10.2.2.2 Artifact Removal
190(1)
10.2.3 Image Segmentation
191(1)
10.2.3.1 Thresholding-Based Segmentation
192(1)
10.2.3.2 Edge-Based Segmentation
192(1)
10.2.3.3 Region-Based Segmentation
193(1)
10.2.3.4 Active Contours-Based Segmentation
193(1)
10.2.3.5 Artificial Intelligence-Based Segmentation
194(1)
10.2.4 Feature Extraction
195(1)
10.2.4.1 Color-based Features
196(1)
10.2.4.2 Dimensional Features
196(1)
10.2.4.3 Texture-Based Features
196(1)
10.2.4.4 Dermoscopic Rules and Methods
197(3)
10.2.5 Feature Selection
200(1)
10.2.6 Classification
201(1)
10.2.7 Classification Performance Evaluation
202(1)
10.2.8 Computer-Aided Diagnosis Systems in Dermoscopic Images
203(2)
10.3 Conclusion
205(8)
Acknowledgment
205(1)
References
205(8)
Conclusions 213(2)
Index 215
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.
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