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.
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.
Table of Contents
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
