Healthcare Monitoring and Data Analysis Using IoT : Technologies and Applications.

Format:

Book

Author/Creator:

Jain, Vishal.

Contributor:

Chatterjee, Jyotir Moy.

Kumar, Pradeep.

Kose, Utku.

Series:

Healthcare Technologies

Language:

English

Subjects (All):

Patient monitoring--Technological innovations.

Physical Description:

1 online resource (427 pages)

Edition:

1st ed.

Place of Publication:

Stevenage : Institution of Engineering & Technology, 2022.

Summary:

This edited book covers big data analysis methods of patient data gained via IoT-enabled monitoring systems. The information gathered can be processed to aid clinicians with diagnoses, prognoses and interventions. This book is a great reference to those using, designing, modelling and analysing intelligent healthcare services.

Contents:

Intro

Contents

About the editors

Preface

Acknowledgments

1. COVID-19 pandemic analysis using application of AI | Pawan Whig, Rahul Reddy Nadikattu and Arun Velu

1.1 Introduction

1.2 Literature survey

1.3 Dataset used for analysis

1.4 Various machine learning libraries

1.5 Training and testing

1.6 Bias and variance

1.7 Result

1.8 Conclusion

References

2. M-health: a revolution due to technology in healthcare sector | Mayuri Diwakar Kulkarni, Ashish Suresh Awate and Jyotir Moy Chatterje

2.1 Introduction

2.2 Discussion

2.3 Conclusion and future work

3. Analysis of Big Data in electroencephalography (EEG) | Sagar Motdhare, Garima Mathur and Ravi Kant

3.1 Introduction

3.2 Methodology

3.3 EEG signal recording

3.4 Activity/action of EEG

3.5 EEG applications

3.6 Mathematical model

3.7 Across the boundaries of small sample sizes

3.8 EEG signal analytics and seizure analysis

3.9 EEG digital video

3.10 EEG data storage and its management

3.11 Big Data in epileptic EEG analysis

3.12 Conclusion

3.13 Future scope

4. An analytical study of COVID-19 outbreak | Shipra Gupta, Vijay Kumar, P. Patil and Lajwanti Kishnani

4.1 Introduction

4.2 Review of literature

4.3 Method

4.4 Results

4.5 Discussions

4.6 Precautions

4.7 Conclusions and future scope

Acknowledgment

5. IoT-based smart healthcare monitoring system | Hakan Yuksel

5.1 Introduction

5.2 Related work

5.3 Proposed method

5.4 Result and discussion

5.5 Conclusion and future scope

6. Development of a secured IoMT device with prioritized medical information for tracking and monitoring COVID patients in rural areas | P.K. Jawahar, K. Indragandhi, G. Kannan and Yiu-Wing Leung

6.1 Introduction.

6.2 Security threats in IoMT

6.3 Introduction to COVID-19

6.4 Proposed system architecture

6.5 Conclusion and future scope

7. An IoT-based system for a volumetric estimation of human brain morphological features from magnetic resonance images | S.N. Kumar, A. Lenin Fred, L.R. Jonisha Miriam, H. Ajay Kumar, I. Christina Jane, Parasuraman Padmanabhan and Balazs Gulyas

7.1 Introduction

7.2 Materials and methods

7.3 Results and discussion

7.4 Conclusion and future scope

8. Healthcare monitoring through IoT: security challenges and privacy issues | S.O. Owoeye, A.S. Akinade, K.I. Adenuga and F.O. Durodola

8.1 Introduction

8.2 IoT applications in personalized healthcare

8.3 Challenges of IoT in personalized healthcare

8.4 Security of IoT in personalized healthcare

8.5 Privacy

8.6 Conclusion and future scope

9. E-health natural language processing | Saman Hina, Raheela Asif and Pardeep Kumar

9.1 Unstructured datasets for E-health NLP research

9.2 Annotation challenges dealing with health-care corpora

9.3 NLP methods that can be adopted to tackle semantics for medical text analysis

9.4 E-health and Internet of Things (IoT)

9.5 Contributions required from NLP researchers in health-care applications

9.6 Conclusion and future work

10. Blockchain of things for healthcare asset management | Sajid Nazir, Mohammad Kaleem, Hassan Hamdoun, Jafar Alzubi and Hua Tianfield

10.1 Introduction

10.2 Healthcare asset management

10.3 Challenges and opportunities in healthcare

10.4 Blockchain: concepts and frameworks

10.5 Blockchain of things architecture for healthcare asset management

10.6 Major healthcare application areas

10.7 Conclusion and future work

References.

11. Artificial intelligence: practical primer for clinical research in cardiovascular disease | Shivendra Dubey, Chetan Gupta and Kalpana Rai

11.1 Artificial intelligence

11.2 Traditional statistics versus AI

11.3 Representative algorithms of AI

11.4 Machine power along with big data

11.5 Challenges to implementation

11.6 Conclusion and future work

12. Deep data analysis for COVID-19 outbreak | S.O. Owoeye, O.J. Odeyemi, F.O. Durodola and K.I. Adenuga

12.1 Introduction to deep data analysis

12.2 Deep data analysis for COVID-19

12.3 CNN architectures

12.4 Building the neural network

12.5 Neural network architecture

12.6 Other parameters used to configure the neural network

12.7 Model summary

12.8 Metrics used for evaluation

12.9 Results and evaluation

12.10 Conclusion and future scope

13. Healthcare system using deep learning | J.B. Shajilin Loret and P.C. Sherimon

13.1 Introduction

13.2 History of healthcare deep learning

13.3 Deep learning benefits

13.4 Components of deep learning

13.5 The role of deep learning in healthcare in the future

13.6 Deep learning applications in healthcare

13.7 Conclusion and future work

14. Intelligent classification of ECG signals using machine learning techniques | Kuldeep Singh Kaswan, Anupam Baliyan, Jagjit Singh Dhatterwal, Vishal Jain and Jyotir Moy Chatterjee

14.1 Introduction

14.2 Heart-generated ECG signal

14.3 Filtering parameters least-mean-square algorithm

14.4 Retrieve and classify ECG signals utilizing ML-based techniques

14.5 Artificial neural network (ANN)-based ECG signals

14.6 Classification of ECG signals based fuzzy logic (FL)

14.7 Fourier transform wavelet transforms

14.8 Combination of machine learning and statistical algorithms.

14.9 Conclusion and future work

15. A survey and taxonomy on mutual interference mitigation techniques in wireless body area networks | Neethu Suman and P.C. Neelakantan

15.1 Introduction

15.2 Interference issues in WBAN

15.3 Mutual interference mitigation schemes

15.4 Conclusion and future scope

16. Predicting COVID cases using machine learning, android, and firebase cloud storage | Ritesh Kumar Sinha, Sukant Kishoro Bisoy, Saurabh Kumar, Sai Prasad Sarangi and Utku Kose

16.1 Introduction

16.2 Literature survey

16.3 Implementation and methodology

16.4 Machine learning models

16.5 Introduction to android app

16.6 Result analysis

16.7 Conclusion and future work

17. Technological advancement with artificial intelligence in healthcare | Manas Kumar Yogi, Jyotsna Garikipati and Jyotir Moy Chatterjee

17.1 Introduction

17.2 Literature review

17.3 Disease identification and diagnosis

17.4 Drug discovery and manufacturing

17.5 Electronic health records

17.6 Disease prediction using machine learning

17.7 Fairness

17.8 Data analytics role in healthcare

17.9 Deep learning applications in healthcare

17.10 Conclusion and future scope

18. Changing dynamics on the Internet of Medical Things: challenges and opportunities | Imtiaz Ali Brohi, Najma Imtiaz Ali and Pardeep Kumar

18.1 Introduction

18.2 The applications of Internet of Things

18.3 Healthcare and Internet of Things

18.4 Security in Internet of Medical Things

18.5 Privacy in Internet of Medical Things

18.6 Perception of trust and risk in IoMT

18.7 Conclusion and future scope

19. Internet of Drones (IOD) in medical transport application | G. Prasad, J. Kavya and J. Sahana

19.1 Introduction to unmanned aerial vehicle.

19.2 Internet of Things in Industry 5.0

19.3 Applications in medical transport

19.4 Methodology and approach

19.5 Conclusion and future

20. Blockchain-based Internet of Things (IoT) for healthcare systems: COVID-19 perspective | Anand Sharma, S.R. Biradar, H.K.D. Sarma and N.P. Rana

20.1 Introduction

20.2 IoT in healthcare system

20.3 COVID-19 outbreak

20.4 Blockchain

20.5 Blockchain-based IoT for healthcare systems

20.6 Advantages of proposed system

20.7 Conclusion and future scope

21. Artificial intelligence-based diseases detection and diagnosis in healthcare | Said El Kafhali and Iman El Mir

21.1 Introduction

21.2 Overview of diseases detection and diagnosis techniques

21.3 Supervised learning models

21.4 Unsupervised learning models

21.5 Reinforcement learning models

21.6 Summary of some applications for disease diagnosis in healthcare

21.7 Some open research problems

21.8 Conclusions

Index.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

1-83724-472-3

1-5231-4666-4

1-83953-438-9

OCLC:

1299384235