Signal Processing Driven Machine Learning Techniques for Cardiovascular Data Processing

Coordinators: Tripathy Rajesh Kumar, Pachori Ram Bilas

Language: English

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400 p. · 15x22.8 cm · Paperback
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.

1. Introduction to Cardiovascular Signals and Recording System 2. Detection and localization of Myocardial Infarction from 12-channel ECG signals using signal processing and machine learning 3. Machine Learning or deep learning combined with signal processing for the automated detection of atrial fibrillation using ECG signals 4. Automated Detection of bundle branch block from 12-lead ECG signals using signal processing and machine learning 5. Signal processing coupled with Machine learning or deep learning for the automated detection of shockable ventricular arrhythmia using ECG signals 6. Automated detection of hypertrophy from ECG signals using machine learning-based signal processing techniques 7. Machine learning coupled with the signal processing-based approach for the prediction of depression and anxiety using ECG signals 8. Signal processing combined with machine learning for the automated prediction of blood pressure using PPG recordings 9. Automated detection of hypertension from PPG signals using signal processing-based machine learning technique 10. Signal Processing driven machine learning technique for automated emotion recognition using ECG/PPG signals 11. Signal processing coupled with machine learning for heart sound activity detection using PCG signals 12. Automated detection of various heart valve disorders from PCG signals using signal processing and deep learning techniques

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
  • Provides details regarding the application of various signal processing and machine learning-based methods for cardiovascular signal analysis
  • Covers methodologies as well as experimental results and studies
  • Helps readers understand the use of different cardiac signals such as ECG, PCG, and PPG for the automated detection of heart ailments and other related biomedical applications