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Classification of Epileptic and Healthy Individual Eeg Signals Using Neural Networks

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dc.authoridAYKAT, Sukru/0000-0003-1738-3696
dc.authorscopusid57214818735
dc.authorscopusid8268513100
dc.authorscopusid9744137200
dc.authorwosidEnsari, Tolga/D-3799-2019
dc.authorwosidSenan, Sibel/C-6665-2019
dc.authorwosidAYKAT, Şükrü/IZF-0285-2023
dc.contributor.authorAykat, Sukru
dc.contributor.authorSenan, Sibel
dc.contributor.authorEnsari, Tolga
dc.contributor.authorAykat, Şükrü
dc.date.accessioned2025-02-15T19:35:17Z
dc.date.available2025-02-15T19:35:17Z
dc.date.issued2020
dc.departmentArtuklu Universityen_US
dc.department-temp[Aykat, Sukru] Mardin Artuklu Univ, Midyat Meslek Yuksekokulu, Bilgisayar Programciligi, Mardin, Turkey; [Senan, Sibel; Ensari, Tolga] Istanbul Univ Cerrahpasa, Muhendislik Fak, Bilgisayar Muhendisligi, Istanbul, Turkeyen_US
dc.descriptionAYKAT, Sukru/0000-0003-1738-3696en_US
dc.description.abstractElectroencephalogram (EEG) are signals used for the analysis of the electrical and functional activity of the brain. These signals are commonly used to detect epileptic seizures. The aim of this study is to classify healthy and epileptic individual EEG signals using artificial neural networks (ANN). For this purpose, the open data source of the University of Bonn was used. The success rates of the classification results obtained with the designed ANN model show the effectiveness of this ANN structure in the application under consideration.en_US
dc.description.provenanceSubmitted by GCRIS Admin (gcris@artuklu.edu.tr) on 2025-02-15T19:35:17Z No. of bitstreams: 0en
dc.description.provenanceMade available in DSpace on 2025-02-15T19:35:17Z (GMT). No. of bitstreams: 0 Previous issue date: 2020en
dc.description.woscitationindexConference Proceedings Citation Index - Science
dc.identifier.citationcount0
dc.identifier.doi10.1109/ubmk50275.2020.9219474
dc.identifier.endpage51en_US
dc.identifier.isbn9781728175652
dc.identifier.scopus2-s2.0-85095709922
dc.identifier.scopusqualityN/A
dc.identifier.startpage47en_US
dc.identifier.urihttps://doi.org/10.1109/ubmk50275.2020.9219474
dc.identifier.urihttps://hdl.handle.net/20.500.12514/6009
dc.identifier.wosWOS:000629055500009
dc.identifier.wosqualityN/A
dc.language.isotren_US
dc.publisherIeeeen_US
dc.relation.ispartof5th International Conference on Computer Science and Engineering (UBMK) -- SEP 09-11, 2020 -- Diyarbakir, TURKEYen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectEeg Signalen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectWavelet Transformen_US
dc.titleClassification of Epileptic and Healthy Individual Eeg Signals Using Neural Networksen_US
dc.typeConference Objecten_US
dspace.entity.typePublication
relation.isAuthorOfPublicationa8323742-ae00-482c-a0b2-850db60f4ea8
relation.isAuthorOfPublication.latestForDiscoverya8323742-ae00-482c-a0b2-850db60f4ea8

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