Examining the effect of time and frequency domain features of EEG, EOG, and Chin EMG signals on sleep staging

Abstract

Sleep staging has an effective role in diagnosing sleep disorders. Sleep staging is generally done by a sleep expert through examining Electroencephalogram (EEG), Electrooculogram (EOG), Electromyogram (EMG) signals of the patients and determining the stages of sleep in different time sections. This type of sleep staging is preferred among the sleep experts but because it is rather tiring and time consuming task, attention to the automatic sleep staging systems has been begun to increase. In this study, we obtained EEG, EMG and EOG signals of five healthy people in Meram Faculty of medicine to use in sleep staging and extracted 74 features from them. We analyzed the effects of these features on sleep staging. We utilized from the sequential feature selection algorithm and Artificial Neural Networks in this application. We determined which features are more effective in classification of sleep stages and by this way we tried to guide researchers who will use EEG, EMG and EOG features in sleep staging. The highest classification accuracy was obtained as 69.30% with use of four features.