Signal Processing Driven Machine Learning Techniques for Cardiovascular Data Processing

Rajesh Kumar Tripathy,Ram Bilas Pachori

• Verlag: Academic Press
• Jahr: 2024
• ISBN:
  9780443141409
• Thema: Ciencia de la computación
• Thema: Redes neuronales

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

Inhaltsverzeichnis

• Cover image
• Title page
• Table of Contents
• Copyright
• List of contributors
• Chapter 1: Introduction to cardiovascular signals and automated systems
• Chapter 2: Third-order tensor-based cardiac disease detection from 12-lead ECG signals using deep convolutional neural network
• Chapter 3: Ramanujan filter bank-domain deep CNN for detection of atrial fibrillation using 12-lead ECG
• Chapter 4: Detection of atrial fibrillation using photoplethysmography signals: a systemic review
• Chapter 5: Machine learning-based prediction of depression and anxiety using ECG signals
• Chapter 6: A robust peak detection algorithm for localization and classification of heart sounds in PCG signals
• Chapter 7: Verifying the effectiveness of a Taylor–Fourier filter bank-based PPG signal denoising approach using machine learning
• Chapter 8: Automated detection of hypertension from PPG signals using continuous wavelet transform and transfer learning
• Chapter 9: Automated estimation of blood pressure using PPG recordings: an updated review
• Chapter 10: Time-frequency-domain deep representation learning for detection of heart valve diseases using PCG recordings for IoT-based smart healthcare applications
• Index