Description
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.- Gives advanced methods in signal processing- Includes machine and deep learning methods- Presents experimental case studies
Table of Contents
1. Feature engineering2. Heart rate variability3. Understanding the suitabillity of parametric modeling techniques in detecting the changes in the HRV signals acquired from cannabis consuming and nonconsuming Indian paddy-field workers4. Patient-specific ECG beat classification using EMD and deep learning-based technique5. Empirical wavelet transform and deep learning-based technique for ECG beat classification6. Development of an Internet-of-Things (IoT)-based pill monitoring device for geriatric patients7. Biomedical robotics8. Combating COVID-19 by implying machine learning predictions and projections9. Deep learning methods for analysis of neural signals: From conventional neural network to graph neural network10. Improved extraction of the extreme thermal regions of breast IR images11. New metrics to asses the subtle changes of the heart's electromagnetic field12. The role of optimal and modified lead systems in electrocardiogram13. Adaptive rate EEG processing and machine learning-based efficient recognition of epilepsy14. Multimodal microscopy: A novel low-cost microscope designed for food and biological applications
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- 洋書電子書籍
- Research and Clinic…
