Advanced Methods in Biomedical Signal Processing and Analysis presents state-of-the-art methods in biosignal processing, including recurrence quantification analysis, heart rate variability, analysis of the RRI time-series signals, joint time-frequency analyses, wavelet transforms and wavelet packet decomposition, empirical mode decomposition, modeling of biosignals, Gabor Transform, empirical mode decomposition. The book also gives an understanding of feature extraction, feature ranking, and feature selection methods, while also demonstrating how to apply artificial intelligence and machine learning to biosignal techniques.
1. Advanced Methods in Biomedical Signal Processing and Analysis - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page i 4. Table of Contents - p. Page iv 5. Copyright - p. Page v 6. Contributors - p. Page v 7. List of Illustrations - p. Page viii 8. List of Tables - p. Page ix 9. 1 : Feature engineering methods - p. Page ix 10. 2 : Heart rate variability - p. Page 26 11. 3 : Understanding the suitability of parametric modeling techniques in detecting the changes in the HRV signals acquired from cannabis consuming and nonconsuming Indian paddy-field workers - p. Page 51 12. 4 : Patient-specific ECG beat classification using EMD and deep learning-based technique - p. Page 82 13. 5 : Empirical wavelet transform and deep learning-based technique for ECG beat classification - p. Page 105 14. 6 : Development of an internet of things (IoT)-based pill monitoring device for geriatric patients - p. Page 126 15. 7 : Biomedical robotics - p. Page 156 16. 8 : Combating COVID-19 by employing machine learning predictions and projections - p. Page 171 17. 9 : Deep learning methods for analysis of neural signals: From conventional neural network to graph neural network - p. Page 197 18. 10 : Improved extraction of the extreme thermal regions of breast IR images - p. Page 234 19. 11 : New metrics to assess the subtle changes of the heart's electromagnetic field - p. Page 254 20. 12 : The role of optimal and modified lead systems in electrocardiogram - p. Page 305 21. 13 : Adaptive rate EEG processing and machine learning-based efficient recognition of epilepsy - p. Page 335 22. 14 : Development of a novel low-cost multimodal microscope for food and biological applications - p. Page 370 23. Index - p. Page 403
Advanced Methods in Biomedical Signal Processing and Analysis presents state-of-the-art methods in biosignal processing, including recurrence quantification analysis, heart rate variability, analysis of the RRI time-series signals, joint time-frequency analyses, wavelet transforms and wavelet packet decomposition, empirical mode decomposition, modeling of biosignals, Gabor Transform, empirical mode decomposition. The book also gives an understanding of feature extraction, feature ranking, and feature selection methods, while also demonstrating how to apply artificial intelligence and machine learning to biosignal techniques.
1. Advanced Methods in Biomedical Signal Processing and Analysis - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page i 4. Table of Contents - p. Page iv 5. Copyright - p. Page v 6. Contributors - p. Page v 7. List of Illustrations - p. Page viii 8. List of Tables - p. Page ix 9. 1 : Feature engineering methods - p. Page ix 10. 2 : Heart rate variability - p. Page 26 11. 3 : Understanding the suitability of parametric modeling techniques in detecting the changes in the HRV signals acquired from cannabis consuming and nonconsuming Indian paddy-field workers - p. Page 51 12. 4 : Patient-specific ECG beat classification using EMD and deep learning-based technique - p. Page 82 13. 5 : Empirical wavelet transform and deep learning-based technique for ECG beat classification - p. Page 105 14. 6 : Development of an internet of things (IoT)-based pill monitoring device for geriatric patients - p. Page 126 15. 7 : Biomedical robotics - p. Page 156 16. 8 : Combating COVID-19 by employing machine learning predictions and projections - p. Page 171 17. 9 : Deep learning methods for analysis of neural signals: From conventional neural network to graph neural network - p. Page 197 18. 10 : Improved extraction of the extreme thermal regions of breast IR images - p. Page 234 19. 11 : New metrics to assess the subtle changes of the heart's electromagnetic field - p. Page 254 20. 12 : The role of optimal and modified lead systems in electrocardiogram - p. Page 305 21. 13 : Adaptive rate EEG processing and machine learning-based efficient recognition of epilepsy - p. Page 335 22. 14 : Development of a novel low-cost multimodal microscope for food and biological applications - p. Page 370 23. Index - p. Page 403
