Cepstrum coefficients based sleep stage classification

Oral, Emin Argun
Çodur, Muhammet Mustafa
Özbek, İbrahim Yücel
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.
IEEE Global Conference on Signal and Information Processing (GlobalSIP), (14 - 16 Kasım 2017)


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ORAL, EMİN ARGUN; Çodur, Muhammet Mustafa; ÖZBEK, İBRAHİM YÜCEL (2017-12-01)
Sleep stage binary classification is studied using single channel EEG signals. The proposed approach is composed of two steps. In the first step, cepstrum coefficients based features are obtained from EEC signals using a filter bank approach which is tuned for sleep stage classification in terms of number of filters and their type. In the second step, these features are used with support vector machine approach for classification. It is observed that obtained results are comparable with the published result...
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Citation Formats
E. A. Oral, M. M. Çodur, and İ. Y. Özbek, “Cepstrum coefficients based sleep stage classification,” presented at the IEEE Global Conference on Signal and Information Processing (GlobalSIP), (14 - 16 Kasım 2017), Montreal, QC, Canada, 2017, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/77351.