Sleep arousal events detection using PNN-GBMO classifier based on EEG and ECG signals: A hybrid-learning model
Foremost sleep event is the sudden change of sleep stages, mainly from deep sleep to light sleep. The notion is very effective in the detection of sleep disorders. In this paper, the detection of arousal events is performed using an automatic analysis of EEG and ECG signals. Unlike previous methods,...
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| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | en en |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://eprints.ums.edu.my/id/eprint/30464/1/Sleep%20arousal%20events%20detection%20using%20PNN-GBMO%20classifier%20based%20on%20abstract.pdf https://eprints.ums.edu.my/id/eprint/30464/2/Sleep%20arousal%20events%20detection%20using%20PNN-GBMO%20classifier%20based%20on.pdf https://eprints.ums.edu.my/id/eprint/30464/ https://ieeexplore.ieee.org/document/9260671 |
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