Telemedicine Technologies: Big Data, Deep Learning, Robotics, Mobile and Remote Applications for Global Healthcare

Description

Telemedicine Technologies: Big Data, Deep Learning, Robotics, Mobile and Remote Applications for Global Healthcare illustrates the innovative concepts, methodologies and frameworks that will increase the feasibility of the existing telemedicine system. The book also focuses on showcasing prototypes of remote healthcare systems, thus emphasizing the data processing side that is often recognized as the backbone of any telemedicine system. Cover Telemedicine Technologies Copyright List of Contributors 1 - Mobile Application for Medical Diagnosis 1. Introduction 2. Description of the Application 2.1 Application Features 2.2 Gantt Chart 2.3 Architecture of the Program 2.4 Design of the Modules 2.4.1 Logging in module 2.4.2 Registration module 2.4.3 The human body module 2.4.4 My account module 2.4.5 The statistics sub-module 2.5 Structure of the Database 3. Usage of the Application 3.1 Registration Process and Finding a Presumptive Diagnosis 3.2 Statistics Area 3.3 Update/Modify the Personal Data 4. Conclusions References 2 - Emerging Paradigms in Transform-Based Medical Image Compression for Telemedicine Environment 1. Introduction 1.1 Asynchronous Telemedicine 1.2 Synchronous Video Conferencing or Interactive Telemedicine 1.3 Remote Patient Monitoring 1.4 Mobile Health or m-Health 2. Technical Challenges in Telemedicine 2.1 Motivation of the Chapter 3. Emerging Paradigms in Transform Based Coding 3.1 Ripplet Transform 3.2 Bandelet Transform 3.3 Radon Transform 3.4 Contourlet Transform 3.5 Curvelet Transform 3.6 Discrete Cosine Transform (DCT) 3.7 Haar Wavelet Transform 3.8 Geometric (RST) Transform 3.8.1 Image interpolation 4. Spiht Encoder 5. Experiments and Discussions 5.1 Investigations on Ripplet Transform 5.2 Investigations on Bandelet Transform 5.3 Investigations on Radon Transform 5.4 Investigations on Contourlet Transform 5.5 Investigations on Curvelet Transform 5.6 Investigations on Discrete Cosine Transform 5.7 Investigations on Haar Wavelet Transform 5.8 Investigations on RST (Geometric) Transform 5.9 Investigations on Subjective Assessment 6. Conclusions References 3 - Adopting m-Health in Clinical Practice: A Boon or a Bane? 1. Introduction 2. History and Evolution of Healthcare Communications 3. m-Health: Definition, Concepts and Milestones 4. m-Health Functionalities and Properties 5. Contexts of m-Health Application in Clinical Practice 6. m-Health Benefits in Clinical Practice 6.1 Clinical Communications 6.2 Diagnostics 6.2.1 Evaluating fractures in orthopedic setting 6.2.2 The evaluation of hematuria in surgical setting 6.3 Rehabilitation 6.4 The Role of m-Health in Medication Non-Adherence 6.4.1 Using m-Health to improve medication adherence in HIV-infected patients 6.4.2 Using m-Health to improve medication adherence in patients with hypertension 6.4.3 Using m-Health to improve medication adherence in ischemic heart disease patients 6.4.4 Using m-Health to improve medication adherence in heart failure patients 6.4.5 Using m-Health to improve medication adherence in stroke patients 6.4.6 Using m-Health to monitor glycemic control in diabetes patients 7. Implications 7.1 Implications for Patients 7.2 Implications for Healthcare Workers 7.3 Implications for Stakeholders 8. Future Direction 9. Conclusions ACKNOWLEDGMENTS References 4 - Investigation of Telecardiology System to Detect Cardiac Abnormalities 1. Theoretical Background 2. Overview of Cardiac Disease 3. Methodology 4. Telecommunications 5. Wi-Fi 6. Bluetooth 7. Wearable Device For ECG Monitoring 8. Results and Discussion 9. Simulation of the Proposed Idea 10. Conclusion 11. Future Scope ACKNOWLEDGMENT Further reading 5 - Microaneurysms Detection for Early Diagnosis of Diabetic Retinopathy Using Shape and Steerable Gaussian Features 1. Introduction 2. Diabetic Retinopathy 2.1 Introduction to Microaneurysms 3. Literature Survey 4. Proposed Methodology 4.1 Input Retinal Image 4.2 Preprocessing 4.3 Illumination Equalization 4.4 Contrast Limited Adaptive Histogram Equalization (CLAHE) 4.5 Histogram Equalization 4.6 Gaussian Smoothening Filtration 4.7 MA Candidate Detection 4.7.1 Morphological processing 4.7.2 Binarization 4.8 Feature Extraction 4.8.1 Intensity features 4.8.2 Shape features 4.8.3 Moment invariants 4.8.4 Gaussian features 4.9 Ensemble Classification 5. Results and Discussions 5.1 Database 5.2 Preprocessing 5.3 MA Candidate Detecton 5.3.1 Morphological operation 5.4 Feature Extraction 6. Conclusion References 6 - Telemetry System for Early Detection of Hyperbilirubinemia in Neonates 1. Introduction 1.1 Hyperbilirubinemia 1.1.1 Formation of bilirubin process 2. Existing Method and Technologies 2.1 Clinical Methods 2.1.1 Diazo method 2.1.2 High-performance liquid chromatography (HPLC) 2.1.3 Direct spectrophotometry 2.1.4 Total Serum Bilirubin test 2.1.5 Transcutaneous bilirubinometers 2.1.6 ETCOc analyzer 2.1.7 Estimation of metalloenzymes by atomic absorption spectrophotometer 2.1.8 Enzymatic method 2.2 Advanced Technologies 2.2.1 Biliscan method 3. Computational Image Processing Techniques 3.1 Image Segmentation 3.1.1 Point-based segmentation technique 3.1.2 Region-based segmentation technique 3.2 Pixel Similarity Method 3.3 White Balancing Method 3.4 Feature Extraction 3.4.1 Feature calculation 3.4.2 Results 3.5 Homomorphic Filtering 3.5.1 Bilirubin detection based on homomorphic filtering 4. Introduction to Mobile Communication 4.1 Lossy Compression Technique 4.1.1 Block truncation coding (BTC) 4.1.2 Code vector quantization 4.1.3 Fractal compression 4.1.4 Transform coding 4.1.5 Sub-band coding 4.2 Lossless Compression Techniques 4.2.1 Huffman coding 4.2.2 Run length coding 4.2.3 Arithmetic coding 4.2.4 Set partitioning in hierarchical tree (SPHIT) algorithm 4.2.5 Modified hierarchical prediction and context adaptive model 4.2.6 Modified hierarchical predictive and block-based lossless image coding (MHPBLI) technique 4.2.7 Coefficient Density Adaptive Quantization (CDAQ) approach based lossless image compression technique 5. Hyperbilirubinemia Image Transmission System 5.1 Image Transmission System on Mobile Devices 5.2 Transmission of Image With High Quality Through Erroneous Wireless Network 5.2.1 Modulation technique 5.3 Context-Aware Wireless Networks for Image Transmission 6. Mobile Communication Security 6.1 Public Key Generation Procedure 7. Conclusion References Further Reading 7 - WBAN: Driving e-healthcare Beyond Telemedicine to Remote Health Monitoring: Architecture and Protocols 1. Introduction 2. Telemedicine and Remote Health Monitoring 2.1 Telemedicine 2.2 Remote Health Monitoring 2.2.1 Applications of remote health monitoring 2.2.2 Benefits of remote health monitoring 2.2.3 Challenges in remote health monitoring 2.3 Difference Between Telemedicine and Remote Health Monitoring 3. WBAN and Remote Health Monitoring 3.1 What is WBAN? 3.2 WBAN Versus WSN 3.3 WBAN System Attributes 3.4 Components of a WBAN System 3.5 WBAN Sensor Node Categories 3.6 WBAN Architecture 3.6.1 Sensors/Intra-BAN communication/Tier 1 3.6.2 Communications module/Inter-BAN communications/Tier 2 3.6.3 Medical database servers/Beyond-BAN/Tier 3 3.7 Role of WBAN in Remote Health Monitoring 3.8 A General Architecture of Remote Health Monitoring Using WBAN 4. WBAN Communication Architecture 4.1 Communication Architecture of WBAN 4.1.1 WBAN network structure 4.1.2 WBAN topologies 4.1.3 Network layers for WBAN 4.1.3.1 PHY layer 4.1.3.2 MAC layer 4.1.3.3 Network layer 4.1.3.4 Transport layer 4.1.3.5 Application layer 4.1.4 Desirable properties of a WBAN communication architecture design 4.2 WBAN Standards 4.2.1 IEEE 802.15.4a standard 4.2.2 IEEE 802.15.6 standard 4.2.2.1 IEEE 802.15.6 standard requirements 4.2.2.2 PHY layer specifications of IEEE 802.15.6 4.2.2.3 MAC layer specifications of IEEE 802.15.6 5. WBAN MAC Layer 5.1 Importance of MAC Layer Protocols for WBANs 5.2 Properties of WBAN MAC Layer Protocols 5.3 Channel Access Techniques for MAC Protocols in WBAN 5.4 Modulation Techniques for MAC Protocols in WBAN 5.5 Traffic Adaptive MAC Protocols 6. Wireless Technologies for Remote Health Monitoring 6.1 Bluetooth 6.2 Bluetooth Low Energy (BLE) 6.3 ZigBee 6.4 WLAN 6.5 Ultra-Wideband (UWB) 6.6 UWB-Based MAC Layer Protocols for WBAN 6.6.1 PSMA-based MAC 6.6.2 UWB2 6.6.3 Multi-band UWB MAC 6.6.4 U-MAC 6.6.5 DCC-MAC 6.6.6 Transmit-only MAC 6.7 Medical Radio Services 6.7.1 Industrial, scientific and medical (ISM) radio bands 6.7.2 Medical implant communications service (MICS) 6.7.3 Wireless medical telemetry service (WMTS) 7. Conclusion References 8 - Remote Monitoring of Children With Chronic Illness Using Wearable Vest 1. INTRODUCTION 2. RELATED WORKS 3. PROPOSED SYSTEM 3.1 System Design 3.2 Data Acquisition and Transmission 3.3 Feature Extraction 3.3.1 Time domain features 3.3.2 Frequency domain features 3.4 Online Dictionary Learning (ODL) Algorithm 3.5 k-nearest Neighbor (k-NN) Algorithm 3.6 Proposed Classification Algorithm 4. EXPERIMENTS 4.1 Parameter Settings 4.2 Results and Discussions 5. CHILD MONITORING USING MOBILE APP DISPLAY 6. CONCLUSION REFERENCES 9 - A Predictive Model for Hypertension Diagnosis Using Machine Learning Techniques 1. Introduction 2. Knowledge Discovery and Intelligent Techniques for Hypertension 2.1 Knowledge Discovery 2.2 Intelligent Techniques 2.2.1 Fuzzy logic 2.2.2 Artificial neural network 2.2.3 Multilayer perceptron 2.2.4 Decision tree 2.2.5 C4.5 algorithm 2.2.6 C5 algorithm 2.2.7 Support vector machine 3. Predictivemodel for Hypertension 3.1 Factors Considered for Prediction Purposes 3.1.1 Main diagnosis model factors 3.1.1.1 Age and gender 3.1.1.2 Weight 3.1.1.3 Salt intake 3.1.1.4 Alcohol 3.1.1.5 Medication 3.1.1.6 Family history 3.1.1.7 Smoking level 3.1.1.8 Coffee level 3.1.2 Psychological and stress factors 3.1.2.1 Anger 3.1.2.2 Stress 3.1.2.3 Depression 3.1.2.4 Exercise 3.1.3 Pregnancy model factors 3.1.3.1 Blood pressure reading 3.1.3.2 Number of weeks of pregnancy 3.1.3.3 History of hypertension before pregnancy 3.1.3.4 Proteinuria 3.1.3.5 History of preeclampsia 3.1.3.6 Dysfunctioning organs and thrombocytopenia 3.1.3.7 Diabetes 3.1.3.8 Visual and cerebral disturbance 3.2 Description of the Model Workflow 3.3 Artificial Neural Network Main Model 3.3.1 Hypertension dataset pre-process 3.3.2 Training of the main model 3.4 Decision Tree for PSL Model and PH Model 4. Evaluation 5. User Acceptance 6. Conclusion References 10 - From Telediagnosis to Teletreatment: The Role of Computational Biology and Bioinformatics in Tele-Based Healthcare 1. Introduction 1.1 Distinctness of Tele-Care From Traditional Care 1.2 Available Varieties of Tele-Care 2. Bioinformatics, Telegenomics, Telegenetics and Telemedicine 2.1 Personalized Telehealthcare 3. Categories of Tele-Based Services 3.1 Telediagnosis 3.1.1 MTS application for telediagnosis 3.1.2 Machine learning approach for telediagnosis 3.2 Teleconsultation 3.3 Telenursing 3.3.1 Telenursing system 3.4 Teletreatment and Telerehabilitation 3.5 Tele-Psychiatry 4. Barriers, Ethical Issues and Authorization 4.1 Barriers 4.1.1 Physical licensing 4.1.2 Credentialing 4.1.3 Reimbursement 4.1.4 Technological barrier 4.1.5 Knowledge barrier 4.2 Ethical Challenges 4.3 Telemedicine Authorization 5. Enabling Bioinformatics and Computational Techniques to Improvise Telehealth 5.1 Telegenomics and Telehealth 5.2 Biological–Omics Databases and Telehealth 5.3 Manifolds of Computational Techniques and Telehealth 5.4 Growing Data in Tele-Based Healthcare 5.5 Personal Sensors in Telehealthcare 5.6 ITAREPS: Data Mining Approach to Telehealth 6. Present Opportunities and Future Prospects 6.1 Potential of Telehealth and the World 6.2 Challenges to Tele-Based Healthcare 6.3 Future Prospects 7. Conclusion, Discussion and Future Directions References Further reading 11 - m-Health in Public Health Practice: A Constellation of Current Evidence 1. Introduction 2. m-Health Benefits in Public Health Practice 2.1 Community-Related Approach to Service Delivery 2.1.1 Maternal and child health 2.1.1.1 Antenatal and postnatal care attendance, skilled midwifery and antenatal health 2.1.1.2 Family planning 2.1.1.3 Childhood immunization 2.1.1.4 Maternal and child nutrition 2.2 Non-Communicable Diseases Prevention and Risk Factor Modification 2.2.1 Physical inactivity 2.2.2 Diet control 2.2.3 Smoking 2.2.4 Alcohol use 2.3 Communicable Diseases 2.3.1 Surveillance, outbreak detection and management 2.3.2 Enhancing infectious disease diagnosis, monitoring and treatment 2.4 Environmental and Occupational Health 2.5 Other Areas 3. Conclusion References 12 - The Egyptian-African Telemedicine Network: The Treat and Teach Comprehensive Model 1. Introduction 2. The Team 3. Concept and Action Plan 4. Swot Analysis 5. The Kick Off 6. Success Dynamics 7. Partnerships and Collaborations 8. Services 9. Conclusion References 13 - An Extended Views Based Big Data Model Toward Facilitating Electronic Health Record Analytics 1. Introduction and Background 2. Sematic Views in Context of Proposed Work 3. Big Data Mining With Extended Semantic Views 4. Proposed Solution to Facilitating Electronic Health Record Processing 4.1 Health Record Cluster (HRC) References 14 - Security and Privacy in Remote Healthcare: Issues, Solutions, and Standards 1. INTRODUCTION 2. REMOTE HEALTHCARE 2.1 “Remote Health Care” Versus “Remote Healthcare” 2.2 Benefits of Remote Healthcare 2.3 Challenges in Remote Healthcare 2.4 Remote Health Care Approaches 2.4.1 Telemedicine 2.4.1.1 What is telemedicine and how it works? 2.4.1.2 Infrastructural components of telemedicine 2.4.1.3 General architecture of telemedicine and its functionality 2.4.1.4 Security vulnerability points in telemedicine 2.4.2 Remote health monitoring 2.4.2.1 What is remote health monitoring and how it works? 2.4.2.2 Infrastructural components of remote health monitoring 2.4.2.3 General architecture of remote health monitoring and its functionality 2.4.2.4 Security vulnerability points in remote health monitoring 3. SECURITY & PRIVACY IN REMOTE HEALTHCARE 3.1 Entities in Remote Healthcare: Their Roles, Interests and Impact on Security 3.2 Security and Privacy Threats in Remote Healthcare 3.2.1 Distributed data collection and data transmission 3.2.1.1 Sensor node or device compromise 3.2.1.2 Dynamics of the network of sensors 3.2.1.3 External network eavesdropping 3.2.2 Data collection and processing 3.2.2.1 Honest-but-curious third-party storage services 3.2.2.2 Malicious (fully untrusted) third-party storage 3.3 Security and Privacy Requirements in Remote Healthcare 3.3.1 Requirements for distributed data access security 3.3.1.1 Data access control with revocability 3.3.1.2 Scalability 3.3.1.3 Flexibility with non-repudiation 3.3.1.4 Accountability 3.3.2 Requirements for distributed data storage security 3.3.2.1 Confidentiality 3.3.2.2 Dynamic integrity 3.3.2.3 Dependability 3.4 Privacy Policies and Regulations 3.4.1 Identifying the data owner 3.4.2 Which data can be secreted and which cannot be? 3.4.3 Who is in-charge in case of emergency situations? 3.4.4 Policy reviews 3.5 Security Solutions for Remote Healthcare 3.5.1 Secure and dependable data storage and processing 3.5.1.1 Symmetric and asymmetric encryption systems 3.5.1.2 Anonymous authentication 3.5.1.3 Dynamic integrity assurance: signature, message digests 3.5.2 Cryptographic access control 3.5.3 Security standards 3.5.4 Wireless, Bluetooth, Zigbee security protocols 3.6 Challenges and Trade-Offs 3.6.1 Interoperability 3.6.2 Security versus efficiency 3.6.3 Security versus usability 3.6.4 Security versus availability 3.7 Future of Remote Healthcare Security 4. CONCLUSION ACKNOWLEDGMENTS REFERENCES 15 - Virtual Clinic: A CDSS Assisted Telemedicine Framework 1. Introduction 2. System Model 3. Research Methodology 3.1 Apriori Algorithm 3.2 Inductive Learning Algorithm 3.3 Assigning the Consultant 3.4 Proposed FRBS Assisted Ranking 3.4.1 Components of the FRBS 3.5 Proposed CDSS 4. Results and Discussion 4.1 Suggest the Doctor 4.1.1 Using Apriori algorithm Rules examples 4.1.2 Using Inductive Learning algorithm Rules generation Comparison 4.2 Suggest Medications by Local History 4.2.1 Using Apriori algorithm Rules examples 4.2.2 Using Inductive Learning algorithm Rules generation Comparison 4.3 Suggest Medications by Doctors History 4.3.1 Rules examples using Apriori algorithm 4.3.2 Using Inductive Learning algorithm Rules generation 4.4 Suggest Medications Using BNF Dataset 4.4.1 Rules examples using Apriori algorithm 4.4.2 Using Inductive Learning algorithm 4.4.3 Rules generation 5. Conclusion References Index A B C D E F G H I J K L M N O P Q R S T U V W X Z Back Cover