Automatic sleep stage classification using two facial electrodes
Published: Aug 1, 2008
Abstract
Standard sleep stage classification is based on visual analysis of central EEG, EOG and EMG signals. Automatic analysis with a reduced number of sensors has been studied as an easy alternative to the standard. In this study, a single-channel electro-oculography (EOG) algorithm was developed for separation of wakefulness, SREM, light sleep (S1, S2) and slow wave sleep (S3, S4). The algorithm was developed and tested with 296 subjects. Additional...
Paper Details
Title
Automatic sleep stage classification using two facial electrodes
Published Date
Aug 1, 2008
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