An epileptic seizure detection system based on cepstral analysis and generalized regression neural network

• Autorzy: Yavuz E. , Kasapbaşı M. C. , Eyüpoğlu C. , Yazıcı R.

Identyfikatory

DOI

10.1016/j.bbe.2018.01.002

• Języki publikacji: EN

Abstrakty

EN

This study introduces a new and effective epileptic seizure detection system based on cepstral analysis utilizing generalized regression neural network for classifying electroen-cephalogram (EEG) recordings. The EEG recordings are obtained from an open database which has been widely studied with many different combinations of feature extraction and classification techniques. Cepstral analysis technique is mainly used for speech recognition, seismological problems, mechanical part tests, etc. Utility of cepstral analysis based features in EEG signal classification is explored in the paper. In the proposed study, mel frequency cepstral coefficients (MFCCs) are computed in the feature extraction stage and used in neural network based classification stage. MFCCs are calculated based on a frequency analysis depending on filter bank of approximately critical bandwidths. The experimental results have shown that the proposed method is superior to most of the previous studies using the same dataset in classification accuracy, sensitivity and specificity. This achieved success is the result of applying cepstral analysis technique to extract features. The system is promising to be used in real time seizure detection systems as the neural network adopted in the proposed method is inherently of non-iterative nature.

Słowa kluczowe

EN

epileptic seizure detection; cepstral analysis; electroencephalogram; generalized regression neural network

PL

napad padaczkowy; analiza cepstralna; elektroencefalogram; sieć neuronowa

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2018
• Tom: Vol. 38, no. 2
• Strony: 201--216
• Opis fizyczny: Bibliogr. 51 poz., rys., tab., wykr.

Twórcy

autor

Yavuz E.

• Küçükyalı E5 Kavşağı İnönü Cad. No: 4, Küçükyalı, 34840 İstanbul, Turkey

autor

Kasapbaşı M. C.

• Department of Computer Engineering, Istanbul Commerce University, Istanbul, Turkey

autor

Eyüpoğlu C.

• Department of Computer Engineering, Istanbul Commerce University, Istanbul, Turkey

autor

Yazıcı R.

• Department of Computer Engineering, Istanbul Commerce University, Istanbul, Turkey

Bibliografia

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Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).