Automatic sleep-wake detection using electrooculogram signals

In this study, we developed an automatic algorithm for sleep-wake detection based on Electrooculography (EOG) in healthy and non-healthy patients. Several features were extracted in time and frequency domains from the EOG signal. The artificial neural network (ANN) was used as a classifier. This pilot study consisted of three aims; the first aim was to utilise only the EOG signal for automatic sleep-wake stage detection. The second objective was to investigate which features were the most effective in detecting the sleep-wake phases in healthy and non-healthy individuals. The third important aim is to investigate which suitable and effective channel can be utilized for detecting the sleep-wake stages. The database was built up using 7 healthy subjects and 9 patients with mixed sleep apnoea, sleep apnoea hypopnea syndrome (SAHS), dyssomnia and periodic limb movements of sleep (PLMS). The inter-rater reliability was 91.3%. The sensitivity and specificity were 84.5% and 91.5%, respectively. Cohen's kappa between visual and automatic algorithm in detection of the sleep-wake stages was 0.74.