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. Chapter 1 : Introduction to cardiovascular signals and automated systems - p. Page ix 2. Chapter 2 : Third-order tensor-based cardiac disease detection from 12-lead ECG signals using deep convolutional neural network - p. Page 14 3. Chapter 3 : Ramanujan filter bank-domain deep CNN for detection of atrial fibrillation using 12-lead ECG - p. Page 33 4. Chapter 4 : Detection of atrial fibrillation using photoplethysmography signals: a systemic review - p. Page 45 5. Chapter 5 : Machine learning-based prediction of depression and anxiety using ECG signals - p. Page 59 6. Chapter 6 : A robust peak detection algorithm for localization and classification of heart sounds in PCG signals - p. Page 77 7. Chapter 7 : Verifying the effectiveness of a Taylor–Fourier filter bank-based PPG signal denoising approach using machine learning - p. Page 96 8. Chapter 8 : Automated detection of hypertension from PPG signals using continuous wavelet transform and transfer learning - p. Page 116 9. Chapter 9 : Automated estimation of blood pressure using PPG recordings: an updated review - p. Page 132 10. Chapter 10 : Time-frequency-domain deep representation learning for detection of heart valve diseases using PCG recordings for IoT-based smart healthcare applications - p. Page 145 11. Index - p. Page 163
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. Chapter 1 : Introduction to cardiovascular signals and automated systems - p. Page ix 2. Chapter 2 : Third-order tensor-based cardiac disease detection from 12-lead ECG signals using deep convolutional neural network - p. Page 14 3. Chapter 3 : Ramanujan filter bank-domain deep CNN for detection of atrial fibrillation using 12-lead ECG - p. Page 33 4. Chapter 4 : Detection of atrial fibrillation using photoplethysmography signals: a systemic review - p. Page 45 5. Chapter 5 : Machine learning-based prediction of depression and anxiety using ECG signals - p. Page 59 6. Chapter 6 : A robust peak detection algorithm for localization and classification of heart sounds in PCG signals - p. Page 77 7. Chapter 7 : Verifying the effectiveness of a Taylor–Fourier filter bank-based PPG signal denoising approach using machine learning - p. Page 96 8. Chapter 8 : Automated detection of hypertension from PPG signals using continuous wavelet transform and transfer learning - p. Page 116 9. Chapter 9 : Automated estimation of blood pressure using PPG recordings: an updated review - p. Page 132 10. Chapter 10 : Time-frequency-domain deep representation learning for detection of heart valve diseases using PCG recordings for IoT-based smart healthcare applications - p. Page 145 11. Index - p. Page 163
