Multiple classification of EEG signals and epileptic seizure diagnosis with combined deep learning

Basit Öğe Kaydı
dc.authoridVARLI, Muhammet/0000-0003-3902-4504
dc.contributor.authorVarli, Muhammet
dc.contributor.authorYilmaz, Hakan
dc.date.accessioned2024-09-29T15:57:45Z
dc.date.available2024-09-29T15:57:45Z
dc.date.issued2023
dc.departmentKarabük Üniversitesien_US
dc.description.abstractEpilepsy stands out as one of the common neurological diseases. The neural activity of the brain is observed using electroencephalography (EEG), which allows the diagnosis of epilepsy disease. The aim of this study is to create a combined deep learning model that automatically detects epileptic seizure activity, detection of the epileptic region and classifies EEG signals by using images representing the time-frequency components of the time series EEG signal and numerical values of the raw EEG signals. In the study, 3 different public datasets, CHB-MIT, BernBarcelona and Bonn EEG records were used. This study presents a combined model using the time sequence of EEG signals and time-frequency-image transformations of time-dependent EEG signals. CWT and STFT methods were used to convert signals to images. Two models were created separately with the images created by CWT and STFT methods. In the Bonn dataset average accuracy rates of 99.07 %, 99.28 %, respectively, in binary classifications and 97.60 % and 98.56 %, respectively, in multiple classifications were obtained with scalogram and spectrogram images. In the Bern-Barcelona and CHB-MIT datasets, 95.46 % and 96.23 % accuracy rates were obtained, respectively. The data combinations brought together in 3 different combinations with the Bonn dataset were underwent to 8-fold cross validation and average accuracy rates of 99.21 % (+/- 0.56), 99.50 % (+/- 0.45), and 98.84 % (+/- 1.58) were obtained. The model we created can detect whether there is epileptic seizure activity in EEG data, detection of the epileptic region and classify EEG signals with a high success rate.en_US
dc.identifier.doi10.1016/j.jocs.2023.101943
dc.identifier.issn1877-7503
dc.identifier.issn1877-7511
dc.identifier.scopus2-s2.0-85146613894en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.jocs.2023.101943
dc.identifier.urihttps://hdl.handle.net/20.500.14619/4996
dc.identifier.volume67en_US
dc.identifier.wosWOS:000963128000001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofJournal of Computational Scienceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectEpilepsyen_US
dc.subjectConvolutional Neural Networken_US
dc.subjectRecurrent Neural Networken_US
dc.subjectCombined deep learningen_US
dc.subjectEpileptic seizure diagnosisen_US
dc.titleMultiple classification of EEG signals and epileptic seizure diagnosis with combined deep learningen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu