Advances in Non-Invasive Biomedical Signal Sensing and Processing with Machine Learning [E-Book] / edited by Saeed Mian Qaisar, Humaira Nisar, Abdulhamit Subasi.
This book presents the modern technological advancements and revolutions in the biomedical sector. Progress in the contemporary sensing, Internet of Things (IoT) and machine learning algorithms and architectures have introduced new approaches in the mobile healthcare. A continuous observation of pat...
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Personal Name(s): | Nisar, Humaira, editor |
Qaisar, Saeed Mian, editor / Subasi, Abdulhamit, editor | |
Edition: |
1st edition 2023. |
Imprint: |
Cham :
Springer,
2023
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Physical Description: |
XVII, 373 pages 131 illustrations, 90 illustrations in color (online resource) |
Note: |
englisch |
ISBN: |
9783031232398 |
DOI: |
10.1007/978-3-031-23239-8 |
Subject (LOC): |
- 1. Introduction to non-invasive biomedical signals for healthcare
- 2. Signal Acquisition Preprocessing and Feature Extraction Techniques for Biomedical Signals
- 3. The Role of EEG as Neuro-Markers for Patients with Depression: A systematic Review
- 4. Brain-Computer Interface (BCI) Based on the EEG Signal Decomposition Butterfly Optimization and Machine Learning
- 5. Advances in the analysis of electrocardiogram in context of mass screening: technological trends and application of artificial intelligence anomaly detection
- 6. Application of Wavelet Decomposition and Machine Learning for the sEMG Signal based Gesture Recognition
- 7. Review of EEG Signals Classification using Machine Learning and Deep-learning Techniques
- 8. "Biomedical signal processing and artificial intelligence in EOG signals"
- 9. Peak Spectrogram and Convolutional Neural Network-based Segmentation and Classification for Phonocardiogram Signals
- 10. Eczema skin lesions segmentation using deep neural network (U-net)
- 11. Biomedical signal processing for automated detection of sleep arousals Based on Multi-Physiological Signals with Ensemble learning methods
- 12. Deep Learning Assisted Biofeedback
- 13. Estimations of Emotional Synchronization Indices for Brain regions using Electroencephalogram Signal Analysis
- 14. Recognition Enhancement of Dementia Patients' Working Memory using Entropy-based Features and Local Tangent Space Alignment Algorithm.