An Automated System for Diagnosis of Sleep Apnea Syndrome Using Single-Channel EEG Signal

Today, a large number of people all around the world are affected or threatened by different kinds of sleep disorders, especially sleep apnea syndrome (SAS). Since EEG activities during sleep exhibit physiological and pathological situation of the brain, analyzing the oscillations during various sleep stages are of fundamental importance for the assessment of sleep disorders. On the other hand, recent developments in the fields of signal processing and pattern recognition have prompted sleep researchers to employ accurate and reliable computerized systems in diagnosis processes. The purpose of this paper was to develop a novel automated methodology for diagnosing apneic patients from healthy subjects using single-channel EEG signal. To this end, it was concentrated on the rapid eye movement (REM) sleep. A comprehensive time-domain based feature extraction scheme was proposed for extracting features from various EEG frequency bands of REM sleep. Uninformative features were eliminated by minimal redundancy maximal relevance (mRMR) feature selection algorithm. An ensemble learning method namely random undersampling boosting (RUSBoost) was then utilized for the purpose of classification. The effectiveness and generalizability of the proposed approach were evaluated using k-fold cross-validation technique. The experimental results demonstrate that the proposed scheme can be used as an efficient method for automated SAS screening with regard to promising classification performance in terms of accuracy, sensitivity, and specificity.