A sleep stage classification method using deep learning by extracting the characteristics of frequency domain from a single EEG channel
Author:
Affiliation:
1. Pusan National University,School of Mechanical Engineering,Busan,Korea
Funder
National Research Foundation of Korea
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9828005/9828007/09828168.pdf?arnumber=9828168
Reference39 articles.
1. Automatic sleep stage classification based on sparse deep belief net and combination of multiple classifiers
2. Automatic detection of rapid eye movements (REMs): A machine learning approach
3. Multi-channel EEG-based sleep stage classification with joint collaborative representation and multiple kernel learning
4. An accurate sleep stages classification system using a new class of optimally time-frequency localized three-band wavelet filter bank
5. The New AASM Criteria for Scoring Hypopneas: Impact on the Apnea Hypopnea Index
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1. Sleep disorders: A review on different deep learning algorithm;AIP Conference Proceedings;2024
2. Electroencephalography Signal Processing: A Comprehensive Review and Analysis of Methods and Techniques;Sensors;2023-07-16
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