Signal Processing Driven Machine Leaing Techniques for Cardiovascular Data Processing

by: Rajesh Kumar Tripathy (Editor),Ram Bilas Pachori (Editor)

Publisher: Academic Press

Edition: 1st

Publication Date: 2024/7/2

Language: English

Print Length: 184 pages

ISBN-10: 044314141X

ISBN-13: 9780443141416

Book Description

Signal Processing Driven Machine Leaing Techniques for Cardiovascular Data Processing features recent advances in machine leaing coupled with new signal processing-based methods for cardiovascular data analysis. Topics in this book include machine leaing methods such as supervised leaing, unsupervised leaing, semi-supervised leaing, and meta-leaing 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 leaing and deep leaing (convolutional neural network (CNN), recurrent neural network (RNN), transformer and attention-based models, etc.) techniques for the analysis of cardiac signals. The interpretable machine leaing and deep leaing models combined with signal processing for cardiovascular data analysis are also covered.Provides details regarding the application of various signal processing and machine leaing-based methods for cardiovascular signal analysisCovers methodologies as well as experimental results and studiesHelps 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

About the Author

Signal Processing Driven Machine Leaing Techniques for Cardiovascular Data Processing features recent advances in machine leaing coupled with new signal processing-based methods for cardiovascular data analysis. Topics in this book include machine leaing methods such as supervised leaing, unsupervised leaing, semi-supervised leaing, and meta-leaing 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 leaing and deep leaing (convolutional neural network (CNN), recurrent neural network (RNN), transformer and attention-based models, etc.) techniques for the analysis of cardiac signals. The interpretable machine leaing and deep leaing models combined with signal processing for cardiovascular data analysis are also covered.Provides details regarding the application of various signal processing and machine leaing-based methods for cardiovascular signal analysisCovers methodologies as well as experimental results and studiesHelps 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