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EEG Based Sleep-Awake Classification Using Sample Entropy and Band Power Ratio

Detection and classification of sleep and awake of an individual has potential applications in biomedical engineering, high-risk work places, vigilance monitoring in Advanced Driver Assistance Systems, etc. In this paper we present a method to classify sleep and awake states using Electroencephalogram (EEG) signal. The proposed method makes use of sample entropy measure and band power ratio of EEG signal as suitable features for efficient classification. The classification is performed using Support Vector Machine (SVM) and an average classification accuracy of 96.28% is obtained on performing the classification among 30 subjects.