Cepstrum Coefficients Based Sleep Stage Classification

Abstract

This paper examines filterbank parameters to extract discriminative cepstrum coefficient from EEG signals for sleep stage classification using well-known Support Vector Machine (SVM) algorithm. The proposed method has three main stages as feature extraction, training and classification. In feature extraction step, features are obtained using linear frequency cepstrum coefficients (LFCC) of EEG signals. Then SVM classifier is trained based on the extracted features. In the final step of classification, the class of test subject is estimated by using the trained model. Experimental results show that about an average of 95 percent correct classification rate is achievable for three classes, and this is better than the compared results available in the literature.