Sleep stage classification based on filter bank optimization

Date

2017-12-01

Author

ORAL, EMİN ARGUN
Çodur, Muhammet Mustafa
ÖZBEK, İBRAHİM YÜCEL

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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 results, and therefore, it is promising.

Subject Keywords

EEG signal, Filter hank, SVM, Sleep stages

URI

https://hdl.handle.net/11511/32240

DOI

https://doi.org/10.1109/siu.2017.7960715

Collections

Graduate School of Natural and Applied Sciences, Conference / Seminar

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Citation Formats

E. A. ORAL, M. M. Çodur, and İ. Y. ÖZBEK, “Sleep stage classification based on filter bank optimization,” 2017, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/32240.