Signal Processing Driven Machine Learning Techniques for Cardiovascular Data Processing

1st Edition - June 12, 2024
Editors: Rajesh Kumar Tripathy, Ram Bilas Pachori
Language: English
Paperback ISBN:
9 7 8 - 0 - 4 4 3 - 1 4 1 4 1 - 6
eBook ISBN:
9 7 8 - 0 - 4 4 3 - 1 4 1 4 0 - 9

Signal Processing Driven Machine Learning Techniques for Cardiovascular Data Processing features recent advances in machine learning coupled with new signal processing-based method… Read more

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Signal Processing Driven Machine Learning Techniques for Cardiovascular Data Processing features recent advances in machine learning coupled with new signal processing-based methods for cardiovascular data analysis. Topics in this book include machine learning methods such as supervised learning, unsupervised learning, semi-supervised learning, and meta-learning combined with different signal processing techniques such as multivariate data analysis, time-frequency analysis, multiscale analysis, and feature extraction techniques for the detection of cardiovascular diseases, heart valve disorders, hypertension, and activity monitoring using ECG, PPG, and PCG signals.

In addition, this book also includes the applications of digital signal processing (time-frequency analysis, multiscale decomposition, feature extraction, non-linear analysis, and transform domain methods), machine learning and deep learning (convolutional neural network (CNN), recurrent neural network (RNN), transformer and attention-based models, etc.) techniques for the analysis of cardiac signals. The interpretable machine learning and deep learning models combined with signal processing for cardiovascular data analysis are also covered.

Cover image
Title page
Table of Contents
Copyright
List of contributors
Chapter 1: Introduction to cardiovascular signals and automated systems
Abstract
1.1. Heart conduction system and ECG signal
1.2. Cardiac auscultation and PCG signal
1.3. PPG signal and cardiorespiratory activity
1.4. Future scope of cardiac data processing
1.5. Conclusion
References
Chapter 2: Third-order tensor-based cardiac disease detection from 12-lead ECG signals using deep convolutional neural network
Abstract
2.1. Introduction
2.2. Dataset description
2.3. Proposed method
2.4. Results and discussion
2.5. Conclusion and summary
References
Chapter 3: Ramanujan filter bank-domain deep CNN for detection of atrial fibrillation using 12-lead ECG
Abstract
3.1. Introduction
3.2. 12-lead ECG database
3.3. Proposed approach
3.4. Results and discussion
3.5. Conclusion
References
Chapter 4: Detection of atrial fibrillation using photoplethysmography signals: a systemic review
Abstract
4.1. Introduction
4.2. Methods
4.3. Results and discussion
4.4. Conclusion
References
Chapter 5: Machine learning-based prediction of depression and anxiety using ECG signals
Abstract
5.1. Introduction
5.2. Mental health problems
5.3. Exploratory data analysis and preprocessing
5.4. Feature extraction
5.5. Machine learning-based model for prediction and classification of ECG signals
5.6. Conclusion and future scope
References
Chapter 6: A robust peak detection algorithm for localization and classification of heart sounds in PCG signals
Abstract
6.1. Introduction
6.2. Literature review
6.3. Proposed methodology
6.4. Experimental results
6.5. Results and discussion
6.6. Conclusion
References
Chapter 7: Verifying the effectiveness of a Taylor–Fourier filter bank-based PPG signal denoising approach using machine learning
Abstract
7.1. Introduction
7.2. PPG signal database
7.3. Method
7.4. Results and discussion
7.5. Conclusion
References
Chapter 8: Automated detection of hypertension from PPG signals using continuous wavelet transform and transfer learning
Abstract
8.1. Introduction
8.2. Methodology
8.3. Results
8.4. Discussion
8.5. Conclusion
References
Chapter 9: Automated estimation of blood pressure using PPG recordings: an updated review
Abstract
9.1. Introduction
9.2. Technical approaches and challenges
9.3. Technical issues associated with accurate BP estimation using PPG signals
9.4. Conclusion
References
Chapter 10: Time-frequency-domain deep representation learning for detection of heart valve diseases using PCG recordings for IoT-based smart healthcare applications
Abstract
10.1. Introduction
10.2. Heart sound signal databases
10.3. Proposed method
10.4. Results and discussion
10.5. Conclusion
References
Index

Rajesh Kumar Tripathy

Dr. Rajesh Kumar Tripathy received a Ph.D. degree in the area of Machine Learning for Signal Processing from IIT Guwahati in 2017. He has also received BTech and Mtech degrees in Electronics & Telecommunication and Biomedical Engineering from BPUT, Odisha, and NIT Rourkela. He is currently working as an assistant professor at BITS Pilani Hyderabad, India. He has over five years of experience as an assistant professor in reputed institutions. He has published 65 papers in reputed international journals. He has also published 10 conference papers and 4 book chapters. He has filed one Indian patent in the area of ECG signal processing. Dr. Tripathy has supervised 2 Ph.D. students in machine learning and biomedical signal processing. He has also supervised 5 Mtech projects and 12 Btech projects. Currently, he supervises one Ph.D. student and 8 Btech students as supervisor. Dr. Tripathy is extensively working in the research areas such as Biomedical Signal Processing, Machine Learning and Deep Learning for Healthcare, Natural Language Processing, Time-frequency analysis, graph signal processing, vertex frequency analysis, Medical Image Processing, and Biomedical Embedded system. He received the outstanding potential for excellence in Research award (OPERA) from BITS Pilani in 2018. He has received 22.80 lacs funding from BITS Pilani through an OPERA grant to conduct high-quality research on signal processing and machine learning for healthcare data analysis. He has completed one sponsored project as a co-principal investigator from the CARS project, DRDO, India. His research papers are cited more than 1807 times on Google scholar (accessed on 19/11/2022). He has been listed as one of the top 2% of scientists based on the Elsevier and Stanford University data. Dr. Tripathy has been awarded as a certified senior data scientist from the United States Data Science institute in 2021. He is also working as the associate editor for reputed journals like IEEE Access, Frontiers in Physiology, and IET Electronics Letters. Dr. Tripathy is also working as Academic Editor for Biomed Research International Journal. He has also worked as a session chair at national and international conferences.

Affiliations and expertise

Assistant Professor, EEE Department, BITS Pilani, Hyderabad, India

Ram Bilas Pachori

Ram Bilas Pachori received the B.E. degree with honours in Electronics and Communication Engineering from Rajiv Gandhi Technological University, Bhopal, India in 2001, the M.Tech. and Ph.D. degrees in Electrical Engineering from IIT Kanpur, India in 2003 and 2008, respectively. He worked as a Post-Doctoral Fellow at Charles Delaunay Institute, University of Technology of Troyes, France during 2007-2008. He served as an Assistant Professor at Communication Research Centre, International Institute of Information Technology, Hyderabad, India during 2008-2009. He served as an Assistant Professor at Department of Electrical Engineering, IIT Indore, India during 2009-2013. He worked as an Associate Professor at Department of Electrical Engineering, IIT Indore during 2013-2017 where presently he has been working as a Professor since 2017. Currently, he is also associated with the Centre for Advanced Electronics at IIT Indore. He was a Visiting Professor at Neural Dynamics of Visual Cognition Lab, Free University of Berlin, Germany during July-September, 2022. He has served as a Visiting Professor at School of Medicine, Faculty of Health and Medical Sciences, Taylor’s University, Malaysia during 2018-2019. Previously, he worked as a Visiting Scholar at Intelligent Systems Research Centre, Ulster University, Londonderry, UK during December 2014. His research interests are in the areas of Signal and Image Processing, Biomedical Signal Processing, Nonstationary Signal Processing, Speech Signal Processing, Brain-Computer Interfacing, Machine Learning, and Artificial Intelligence & Internet of Things in Healthcare. He is an Associate Editor of Electronics Letters, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Biomedical Signal Processing and Control and an Editor of IETE Technical Review journal. He is a senior member of IEEE and a Fellow of IETE, IEI, and IET. He has supervised 15 Ph.D., 23 M.Tech., and 42 B.Tech. students for their theses and projects (14 Ph.D., 03 M.Tech., 01 M.S. (by Research), and 07 B.Tech. under progress). He has 270 publications which include journal papers (166), conference papers (74), books (08), and book chapters (22). He has also three patents: 01 Australian patent (granted) and 02 Indian patents (filed). He has worked on various research projects with funding support from SERB, DST, DBT, CSIR, and ICMR.

Affiliations and expertise

Professor, EE Department, IIT Indore, India

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