Generalized filter bank design for sleep stage classification

Date

2017-11-02

Author

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

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In this study, binary sleep stage classification (sleep or awake state) was performed using single-channel EEG signal. A new frequency warping function is proposed for this purpose. This function provides a bending function that can proper orientation and depth of the EEG signal frequency content. In this way a generalized filter set of was designed. With the help of this filter set, cepstrum features are extracted. In classification stage, Support Vector Machines (SVM) are employed because of its good performance at binary classification. According to the experimental results, the highest correct classification rate(accuracy) is 98.40%. The result is better than studies which use same database in literature.

Subject Keywords

EEG signal, Filter bank, SVM, Sleep stages

URI

https://hdl.handle.net/11511/30015

DOI

https://doi.org/10.1109/idap.2017.8090175

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, “Generalized filter bank design for sleep stage classification,” 2017, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/30015.