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

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dc.contributor.authorYavuz, Erdem
dc.contributor.authorKasapbaşı, Mustafa Cem
dc.contributor.authorEyüpoğlu, Can
dc.contributor.authorYazıcı, Rıfat
dc.date.accessioned2020-11-21T15:53:23Z
dc.date.available2020-11-21T15:53:23Z
dc.date.issued2018en_US
dc.departmentİstanbul Ticaret Üniversitesien_US
dc.description.abstractThis study introduces a new and effective epileptic seizure detection system based on cepstral analysis utilizing generalized regression neural network for classifying electroencephalogram (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. © 2018 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciencesen_US
dc.identifier.doi10.1016/j.bbe.2018.01.002en_US
dc.identifier.endpage216en_US
dc.identifier.issn0208-5216
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85042420652en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage201en_US
dc.identifier.urihttps://doi.org/10.1016/j.bbe.2018.01.002
dc.identifier.urihttps://hdl.handle.net/11467/3566
dc.identifier.volume38en_US
dc.identifier.wosWOS:000432621000001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPWN-Polish Scientific Publishersen_US
dc.relation.ispartofBiocybernetics and Biomedical Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCepstral analysisen_US
dc.subjectElectroencephalogramen_US
dc.subjectEpileptic seizure detectionen_US
dc.subjectGeneralized regression neural networken_US
dc.titleAn epileptic seizure detection system based on cepstral analysis and generalized regression neural networken_US
dc.typeArticleen_US

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