Abstract:Background: Conventional manual sleep stage classification is time-consuming and relies on the knowledge and experience of the specialists. The emergence of automatic sleep stage classification greatly improves the classification efficiency. The feature extraction in automatic sleep stage classification is particularly important, which usually uses the linear methods based on techniques in the time domain, frequency domain, or time-frequency domain. Electroencephalograms (EEGs) contain a wealth of physiological info… Show more
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