EEG signal features for computer-aided sleep stage detection

Abstract

Sleep apnea is a disorder in which individuals stop breathing during their sleep. Sleep apnea is categorized as obstructive, central or mixed. New techniques for sleep stage classification are being developed by bioengineers and clinicians for appropriate and timely detection of sleep disorders. The material presented in this work, includes a compendium of features extracted from the sleep studies of patients suffering from sleep apnea. Twenty-five subjects (21 males and 4 females) were selected (age: 50 plusmn 10 years, range 28-68 years) data was available online at the physionet database. Time and frequency domain algorithms were applied to polysomnographic signals such as EEG, EMG, and EOG signals. Results show that trends provided by this indicators could be used to automatically distinguish between sleep stages at a highly significant level (p < 0.01.) This could prove very helpful in sleep apnea detection.