Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14365/5615
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dc.contributor.authorAkbugday, Sude Pehlivan-
dc.contributor.authorCura, Ozlem Karabiber-
dc.contributor.authorAkbugday, Burak-
dc.contributor.authorAkan, Aydin-
dc.date.accessioned2024-11-25T16:53:56Z-
dc.date.available2024-11-25T16:53:56Z-
dc.date.issued2024-
dc.identifier.isbn9789464593617-
dc.identifier.isbn9798331519773-
dc.identifier.issn2076-1465-
dc.identifier.urihttps://doi.org/10.23919/EUSIPCO63174.2024.10714940-
dc.description.abstractOne of the most frequent neurological conditions that impair cognitive abilities and have a major negative impact on quality of life is dementia. In this work, a novel approach for identifying Alzheimer's disease (AD) by utilizing electroencephalogram (EEG) signals via signal processing techniques is proposed. Five spectral domain characteristics are computed for one-minute EEG segment duration using EEG data. Each feature is mapped onto a 9 x 9 matrix called topographic EEG feature maps (EEG-FM) to represent spectral as well as spatial information on the same image. Images were then classified using a 2-layer convolutional neural network (CNN) to classify healthy and AD cases. Results indicate that the constructed CNN generalizes well, and the proposed method can accurately classify AD from EEG-FMs with up to %99 accuracy, precision, and recall with loss values as low as 0.01.en_US
dc.description.sponsorshipIzmir University of Economics, Scientific Research Projects Coordination Unit [2022-07]en_US
dc.description.sponsorshipThis study was partially supported by Izmir University of Economics, Scientific Research Projects Coordination Unit. Project number: 2022-07.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartof32nd European Signal Processing Conference (EUSIPCO) -- AUG 26-30, 2024 -- Lyon, FRANCEen_US
dc.relation.ispartofseriesEuropean Signal Processing Conference-
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAlzheimer'S Dementia (Ad)en_US
dc.subjectEeg Feature Maps (Eeg-Fm)en_US
dc.subjectDeep Learningen_US
dc.subjectCnnen_US
dc.titleDetection of Alzheimer's Dementia by Using Eeg Feature Maps and Deep Learningen_US
dc.typeConference Objecten_US
dc.identifier.doi10.23919/EUSIPCO63174.2024.10714940-
dc.identifier.scopus2-s2.0-85208436535-
dc.departmentİzmir Ekonomi Üniversitesien_US
dc.authorwosidAkbugday, Burak/Gso-0234-2022-
dc.authorscopusid57215310544-
dc.authorscopusid57195223021-
dc.authorscopusid57211987353-
dc.authorscopusid35617283100-
dc.identifier.startpage1397en_US
dc.identifier.endpage1401en_US
dc.identifier.wosWOS:001349787000280-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityN/A-
dc.identifier.wosqualityN/A-
dc.description.woscitationindexConference Proceedings Citation Index - Science-
item.openairetypeConference Object-
item.grantfulltextreserved-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextWith Fulltext-
crisitem.author.dept05.06. Electrical and Electronics Engineering-
crisitem.author.dept05.06. Electrical and Electronics Engineering-
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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