The Internet of Medical Things (IoMT)

1 Cover

2 Title Page

3 Copyright

4 Preface

5 1 In Silico Molecular Modeling and Docking Analysis in Lung Cancer Cell Proteins 1.1 Introduction 1.2 Methodology 1.3 Results and Discussion 1.4 Conclusion References

6 2 Medical Data Classification in Cloud Computing Using Soft Computing With Voting Classifier: A Review 2.1 Introduction 2.2 Access Control–Based Security 2.3 System Model 2.4 Data Classification 2.5 Related Work 2.6 Conclusion References

7 3 Research Challenges in Pre-Copy Virtual Machine Migration in Cloud Environment 3.1 Introduction 3.2 Existing Technology and Its Review 3.3 Research Design 3.4 Results 3.5 Discussion 3.6 Conclusion References

8 4 Estimation and Analysis of Prediction Rate of Pre-Trained Deep Learning Network in Classification of Brain Tumor MRI Images 4.1 Introduction 4.2 Classes of Brain Tumors 4.3 Literature Survey 4.4 Methodology 4.5 Conclusion References

9 5 An Intelligent Healthcare Monitoring System for Coma Patients 5.1 Introduction 5.2 Related Works 5.3 Materials and Methods 5.4 Results and Discussion 5.5 Conclusion References

10 6 Deep Learning Interpretation of Biomedical Data 6.1 Introduction 6.2 Deep Learning Models 6.3 Interpretation of Deep Learning With Biomedical Data 6.4 Conclusion References

11 7 Evolution of Electronic Health Records 7.1 Introduction 7.2 Traditional Paper Method 7.3 IoMT 7.4 Telemedicine and IoMT 7.5 Cyber Security 7.6 Materials and Methods 7.7 Literature Review 7.8 Applications of Electronic Health Records 7.9 Results and Discussion 7.10 Challenges Ahead 7.11 Conclusion References

12 8 Architecture of IoMT in Healthcare 8.1 Introduction 8.2 Preferences of the Internet of Things 8.3 IoMT Progress in COVID-19 Situations: Presentation 8.4 Major Applications of IoMT References

13 9 Performance Assessment of IoMT Services and Protocols 9.1 Introduction 9.2 IoMT Architecture and Platform 9.3 Types of Protocols 9.4 Testing Process in IoMT 9.5 Issues and Challenges 9.6 Conclusion References

14 10 Performance Evaluation of Wearable IoT-Enabled Mesh Network for Rural Health Monitoring 10.1 Introduction 10.2 Proposed System Framework 10.3 Experimental Evaluation 10.4 Performance Evaluation 10.5 Conclusion References

15 11 Management of Diabetes Mellitus (DM) for Children and Adults Based on Internet of Things (IoT) 11.1 Introduction 11.2 Materials and Methods 11.3 Results and Discussion 11.4 Summary 11.5 Conclusion References

16 12 Wearable Health Monitoring Systems Using IoMT 12.1 Introduction 12.2 IoMT in Developing Wearable Health Surveillance System 12.3 Vital Parameters That Can Be Monitored Using Wearable Devices 12.4 Challenges Faced in Customizing Wearable Devices 12.5 Conclusion References

17 13 Future of Healthcare: Biomedical Big Data Analysis and IoMT 13.1 Introduction 13.2 Big Data and IoT in the Healthcare Industry 13.3 Biomedical Big Data Types 13.4 Biomedical Data Acquisition Using IoT 13.5 Biomedical Data Management Using IoT 13.6 Impact of Big Data and IoMT in Healthcare 13.7 Discussions and Conclusions References

18 14 Medical Data Security Using Blockchain With Soft Computing Techniques: A Review 14.1 Introduction 14.2 Blockchain 14.3 Blockchain as a Decentralized Security Framework 14.4 Existing Healthcare Data Predictive Analytics Using Soft Computing Techniques in Data Science 14.5 Literature Review: Medical Data Security in Cloud Storage 14.6 Conclusion References

19 15 Electronic Health Records: A Transitional View 15.1 Introduction 15.2 Ancient Medical Record, 1600 BC 15.3 Greek Medical Record 15.4 Islamic Medical Record 15.5 European Civilization 15.6 Swedish Health Record System 15.7 French and German Contributions 15.8 American Descriptions 15.9 Beginning of Electronic Health Recording 15.10 Conclusion References

20 Index

21 End User License Agreement

1 Chapter 1 Figure 1.1 SOPMA plots for (a) EGFR, (b) K-ras oncogene protein, and (c) TP53. Figure 1.2 Ramachandran plots for (a) EGFR, (b) K-ras oncogene protein, and (c) ... Figure 1.3 ERRAT plots for (a) EGFR, (b) K-ras oncogene protein, and (c) TP53. Figure 1.4 Verify 3D plots for (a) EGFR, (b) K-ras oncogene protein, and (c) TP5...

2 Chapter 2 Figure 2.1 Architecture for PHR system. Figure 2.2 Data classification in cloud computing. company information and resou... Figure 2.3 Fuzzy classification block diagram. Figure 2.4 Analysis framework architecture.

3 Chapter 3 Figure 3.1 Cloud computing. Figure 3.2 Types of cloud. Figure 3.3 Virtualization.Figure 3.4 Phases of virtual machine migration.Figure 3.5 Pre-copy algorithm.Figure 3.6 Timeline for pre-copy.Figure 3.7 Improved pre-copy live migration.Figure 3.8 Iteration of time series–based pre-copy live migration.Figure 3.9 Structure and operations of MPLM.Figure 3.10 Structure of TPO.Figure 3.11 Structure of multiphase pre-copy live migration.Figure 3.12 Pre-copy vs. improved pre-copy.Figure 3.13 Low dirty and high dirty pages based on time series.Figure 3.14 Pre-copy vs. TPO.Figure 3.15 TPO vs. multiphase.

4 Chapter 4Figure 4.1 Design flow of the proposed system.Figure 4.2 Comparative analysis of pre-trained networks for brain tumor classifi...

5 Chapter 5Figure 5.1 Block diagram.Figure 5.2 Pulse oximeter and heart rate sensor.Figure 5.3 Temperature sensor.Figure 5.4 Complete hardware of the coma patient monitoring system.Figure 5.5 Eye blink detection.Figure 5.6 Yawning detection.Figure 5.7 Alert message page.Figure 5.8 Subject testing.Figure 5.9 Results of heart rate and SpO 2.Figure 5.10 EEG patterns in coma patient.

6 Chapter 6Figure 6.1 Basic structural representation of deep learning process.Figure 6.2 Simple architecture with hidden layers.Figure 6.3 Architectural models of deep learning.Figure 6.4 Architecture of recurrent neural networks.Figure 6.5 LSTM memory cell.Figure 6.6 GRU cell.Figure 6.7 Basic structural framework of convolutional neural network (CNN).Figure 6.8 Architectural framework for deep belief networks.Figure 6.9 Simple architecture of deep stacking networks.

7 Chapter 9Figure 9.1 IoMT healthcare system.Figure 9.2 Platform architecture.Figure 9.3 Communication protocol overview.Figure 9.4 The MQTT publish and subscribe model for IoT.Figure 9.5 CoAP message model.Figure 9.6 CoAP request/response model.Figure 9.7 AMQP interaction model with middleware.Figure 9.8 AMQP capabilities.Figure 9.9 AMQP for cloud connection.Figure 9.10 DDS protocol Architecture.

8 Chapter 10Figure 10.1 Architecture of wearable IoT-enabled rural health monitoring system.Figure 10.2 Body sensor node and its internal architecture.Figure 10.3 System framework of health monitoring center (HMC).Figure 10.4 Sequence diagrams of mesh peering and routing medical data.Figure 10.5 GUI alert when the patient’s blood pressure and sugar is critically ...Figure 10.6 Energy consumption in HMC.Figure 10.7 Survival rate.Figure 10.8 End-to-end delay.

9 Chapter 11Figure 11.1 Block diagram of the proposed system.Figure 11.2 Components of the noninvasive glucose monitoring system.Figure 11.3 Prototype of the glucose monitoring system.Figure 11.4 Output of glucose monitoring.

10 Chapter 13Figure 13.1 Three important V of big data.Figure 13.2 Applications of big data and IoT in healthcare.Figure 13.3 Basic details available in an electronic health record.Figure 13.4 Commonly used big data management tools.

11 Chapter 14Figure 14.1 General architecture and workflow of the proposed system [7].Figure 14.2 Remote patient monitoring [8].Figure 14.3 Blockchain architecture categories [7].Figure 14.4 Nodes in public vs. private Blockchain [8].Figure 14.5 Scenarios of using Blockchain in different healthcare situations [8]...Figure 14.6 Potential applications of the Blockchain [10].Figure 14.7 Characteristics of Blockchain.