Advanced methods in biomedical signal processing and analysis /

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Bibliographic Details
Imprint:Amsterdam : Academic Press, 2022.
Description:1 online resource
Language:English
Subject:
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/13544686
Hidden Bibliographic Details
Other authors / contributors:Pal, Kunal, editor.
Ari, Samit, editor.
Bit, Arindam, 1985- editor.
Bhattacharyya, Saugat, editor.
ISBN:0323859542
9780323859554
0323859550
9780323859547
Notes:

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


Description based on CIP data; resource not viewed.
Summary: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.
Other form:Print version: 9780323859554