Epilepsy Diagnosis from EEG Signals Using Continuous Wavelet Transform-Based Depthwise Convolutional Neural Network Model

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dc.authoridGuldemir, Hanifi/0000-0003-0491-8348
dc.authoridSengur, Abdulkadir/0000-0003-1614-2639
dc.authoridDISLI, FIRAT/0000-0003-0016-3558
dc.contributor.authorDisli, Firat
dc.contributor.authorGedikpinar, Mehmet
dc.contributor.authorFirat, Huseyin
dc.contributor.authorSengur, Abdulkadir
dc.contributor.authorGuldemir, Hanifi
dc.contributor.authorKoundal, Deepika
dc.date.accessioned2025-02-22T14:08:40Z
dc.date.available2025-02-22T14:08:40Z
dc.date.issued2025
dc.departmentDicle Üniversitesien_US
dc.description.abstractBackground/Objectives: Epilepsy is a prevalent neurological disorder characterized by seizures that significantly impact individuals and their social environments. Given the unpredictable nature of epileptic seizures, developing automated epilepsy diagnosis systems is increasingly important. Epilepsy diagnosis traditionally relies on analyzing EEG signals, with recent deep learning methods gaining prominence due to their ability to bypass manual feature extraction. Methods: This study proposes a continuous wavelet transform-based depthwise convolutional neural network (DCNN) for epilepsy diagnosis. The 35-channel EEG signals were transformed into 35-channel images using continuous wavelet transform. These images were then concatenated horizontally and vertically into a single image (seven rows by five columns) using Python's PIL library, which served as input for training the DCNN model. Results: The proposed model achieved impressive performance metrics on unseen test data: 95.99% accuracy, 94.27% sensitivity, 97.29% specificity, and 96.34% precision. Comparative analyses with previous studies and state-of-the-art models demonstrated the superior performance of the DCNN model and image concatenation technique. Conclusions: Unlike earlier works, this approach did not employ additional classifiers or feature selection algorithms. The developed model and image concatenation method offer a novel methodology for epilepsy diagnosis that can be extended to different datasets, potentially providing a valuable tool to support neurologists globally.en_US
dc.description.sponsorshipFirat University [TEKF.24.50]; Firat University, Scientific Research Project Committeeen_US
dc.description.sponsorshipThis study was supported by Firat University, Scientific Research Project Committee, under grant no: TEKF.24.50.en_US
dc.identifier.doi10.3390/diagnostics15010084
dc.identifier.issn2075-4418
dc.identifier.issue1en_US
dc.identifier.pmid39795612en_US
dc.identifier.scopus2-s2.0-85215506152en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.3390/diagnostics15010084
dc.identifier.urihttps://hdl.handle.net/11468/29557
dc.identifier.volume15en_US
dc.identifier.wosWOS:001393951300001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofDiagnosticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzKA_WOS_20250222
dc.subjectepilepsyen_US
dc.subjectdepthwise convolutionen_US
dc.subjectimage concatenateen_US
dc.subjectcontinuous wavelet transformen_US
dc.titleEpilepsy Diagnosis from EEG Signals Using Continuous Wavelet Transform-Based Depthwise Convolutional Neural Network Modelen_US
dc.typeArticleen_US

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