Engineering and technology for healthcare /
[edited by] Muhammad Ali Imran, Rami Ghannam, Qammer H. Abbasi.
- 1 PDF.
Includes bibliographical references and index.
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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"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"--