Wei Pei1, Yan Li1, Siuly Siuly1,*, Peng Wen2
CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 889-905, 2022, DOI:10.32604/cmc.2022.021830
Abstract Sleep stage classification plays a significant role in the accurate diagnosis and treatment of sleep-related diseases. This study aims to develop an efficient deep learning based scheme for correctly identifying sleep stages using multi-biological signals such as electroencephalography (EEG), electrocardiogram (ECG), electromyogram (EMG), and electrooculogram (EOG). Most of the prior studies in sleep stage classification focus on hand-crafted feature extraction methods. Traditional hand-crafted feature extraction methods choose features manually from raw data, which is tedious, and these features are limited in their ability to balance efficiency and accuracy. Moreover, most of the existing works on sleep staging are either single… More >
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