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Full 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.
Contents
1. Feature engineering
2. Heart rate variability
3. Understanding the suitabillity of parametric modeling techniques in detecting the changes in the HRV signals acquired from cannabis consuming and nonconsuming Indian paddy-field workers
4. Patient-specific ECG beat classification using EMD and deep learning-based technique
5. Empirical wavelet transform and deep learning-based technique for ECG beat classification
6. Development of an Internet-of-Things (IoT)-based pill monitoring device for geriatric patients
7. Biomedical robotics
8. Combating COVID-19 by implying machine learning predictions and projections
9. Deep learning methods for analysis of neural signals: From conventional neural network to graph neural network
10. Improved extraction of the extreme thermal regions of breast IR images
11. New metrics to asses the subtle changes of the heart's electromagnetic field
12. The role of optimal and modified lead systems in electrocardiogram
13. Adaptive rate EEG processing and machine learning-based efficient recognition of epilepsy
14. Multimodal microscopy: A novel low-cost microscope designed for food and biological applications
