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Detecting Slow Wave Sleep Using A Single Eeg Signal Channel, Bo-Lin Su, Yuxi Luo, Chih-Yuan Hong, Mark L. Nagurka, Chen-Wen Yen

Mechanical Engineering Faculty Research and Publications

Background: In addition to the cost and complexity of processing multiple signal channels, manual sleep staging is also tedious, time consuming, and error-prone. The aim of this paper is to propose an automatic slow wave sleep (SWS) detection method that uses only one channel of the electroencephalography (EEG) signal.

New Method: The proposed approach distinguishes itself from previous automatic sleep staging methods by using three specially designed feature groups. The first feature group characterizes the waveform pattern of the EEG signal. The remaining two feature groups are developed to resolve the difficulties caused by interpersonal EEG signal differences.

Results and …