A Hybrid Study for Epileptic Seizure Detection Based on Deep Learning using EEG Data

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dc.authoridKaplan, Kaplan/0000-0001-8036-1145
dc.authoridBuldu, Abdulkadir/0000-0002-9161-4862
dc.contributor.authorBuldu, Abdulkadir
dc.contributor.authorKaplan, Kaplan
dc.contributor.authorKuncan, Melih
dc.date.accessioned2024-12-24T19:33:57Z
dc.date.available2024-12-24T19:33:57Z
dc.date.issued2024
dc.departmentSiirt Üniversitesi
dc.description.abstractEpilepsy, a neurological disease characterized by recurrent seizures, can be diagnosed using Electroencephalogram (EEG) signals. Traditional diagnostic methods often face limitations, leading to delays and potential misdiagnoses. In response, researchers have been developing low-cost assistive systems to enhance diagnostic accuracy and reduce life-threatening risks for epilepsy patients. In this study, a hybrid approach is proposed to diagnose epilepsy disease. To validate the success of the proposed algorithm, Hauz Khas and Bonn data sets were used. AlexNet, GoogleNet, VGG19, ResNet50, and ResNet101 classifiers were employed in this study along with the Continuous Wavelet Transform (CWT) and Short Time Fourier Transform (STFT). To increase the generalization capability, 10-fold cross-validation method was used in the classification process. Firstly, the preictal and ictal moments in the Hauz Khas dataset was classified with 99.5% success rate by CWT method and Resnet101. Similarly, 99.8% accuracy was achieved in the binary classification of the Bonn dataset using the CWT method with Resnet101. Finally, for the classification with the AB-CD-E group, 99.33% classification success rate was achieved by using the CWT method with the Resnet-101 model. These findings underscore the potential of the proposed assistive system to significantly improve the diagnosis and management of epilepsy, demonstrating high accuracy and reliability across different datasets and classification techniques.
dc.identifier.doi10.3897/jucs.109933
dc.identifier.issn0948-695X
dc.identifier.issn0948-6968
dc.identifier.issue7
dc.identifier.scopus2-s2.0-85201646651
dc.identifier.scopusqualityQ3
dc.identifier.urihttps://doi.org/10.3897/jucs.109933
dc.identifier.urihttps://hdl.handle.net/20.500.12604/8342
dc.identifier.volume30
dc.identifier.wosWOS:001301587500002
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherGraz Univ Technolgoy, Inst Information Systems Computer Media-Iicm
dc.relation.ispartofJournal of Universal Computer Science
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241222
dc.subjectEEG
dc.subjectepilepsy diagnosis
dc.subjectSTFT
dc.subjectCWT
dc.subjecttransfer learning
dc.titleA Hybrid Study for Epileptic Seizure Detection Based on Deep Learning using EEG Data
dc.typeArticle

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