Signal Processing Driven Machine Learning Techniques for Cardiovascular Data Processing (eBook, ePUB)

Redaktion: Tripathy, Rajesh Kumar; Pachori, Ram Bilas

• Format: ePub

Produktbeschreibung

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.

• 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

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Produktdetails

• Verlag: Elsevier Science & Techn.
• Seitenzahl: 400
• Erscheinungstermin: 12. Juni 2024
• Englisch
• ISBN-13: 9780443141409
• Artikelnr.: 72252166

Inhaltsangabe

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