||Currently, sleep staging is accomplished is by clinical polysomnography (PSG). By extracting features from different combinations of electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG) signals, this study uses neural network to perform sleep staging.|
A whole night and complete sleep stage contains wake stage, rapid eye movement (REM) stage, stage 1, stage 2, and slow wave sleep (SWS) stage. This project focuses on the classification of wake stage and light-sleep (stage 1 and 2). These three stages are classified by a two-step process. At first, wake stage and light-sleep are divided into two parts. Second, light sleep is divided into stage 1 and stage 2. For a fixed number of channels, this work identifies the best combination of signal channels. In addition, by simultaneously considering the Neighboring epochs Rule classifier, this work also introduces an empirical rule to improve the classification accuracy.
Among the tested databases which contain two Medicine center and sixteen sets of different signal channels, the best results are obtained from the group of patients with the low average RDI value. They include the group that has a mean 15% SWS and the group that uses CPAP. As a whole, the combinative features of four channels are better results of classification. For our best results, the sensitivity and PPV of wake and stage 2 varies from 85%~88%, and those of stage 1 are respective 44.84% and 53.61%. And the total classification of sleep staging is 84.59%. Apparently, the research has satisfactory results on sleep staging.
Keywords: Sleep Medicine, Sleep stage, Neural Networks