Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14365/5615
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Akbugday, Sude Pehlivan | - |
dc.contributor.author | Cura, Ozlem Karabiber | - |
dc.contributor.author | Akbugday, Burak | - |
dc.contributor.author | Akan, Aydin | - |
dc.date.accessioned | 2024-11-25T16:53:56Z | - |
dc.date.available | 2024-11-25T16:53:56Z | - |
dc.date.issued | 2024 | - |
dc.identifier.isbn | 9789464593617 | - |
dc.identifier.isbn | 9798331519773 | - |
dc.identifier.issn | 2076-1465 | - |
dc.identifier.uri | https://doi.org/10.23919/EUSIPCO63174.2024.10714940 | - |
dc.description.abstract | One 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.sponsorship | Izmir University of Economics, Scientific Research Projects Coordination Unit [2022-07] | en_US |
dc.description.sponsorship | This study was partially supported by Izmir University of Economics, Scientific Research Projects Coordination Unit. Project number: 2022-07. | en_US |
dc.language.iso | en | en_US |
dc.publisher | IEEE | en_US |
dc.relation.ispartof | 32nd European Signal Processing Conference (EUSIPCO) -- AUG 26-30, 2024 -- Lyon, FRANCE | en_US |
dc.relation.ispartofseries | European Signal Processing Conference | - |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Alzheimer'S Dementia (Ad) | en_US |
dc.subject | Eeg Feature Maps (Eeg-Fm) | en_US |
dc.subject | Deep Learning | en_US |
dc.subject | Cnn | en_US |
dc.title | Detection of Alzheimer's Dementia by Using Eeg Feature Maps and Deep Learning | en_US |
dc.type | Conference Object | en_US |
dc.identifier.doi | 10.23919/EUSIPCO63174.2024.10714940 | - |
dc.identifier.scopus | 2-s2.0-85208436535 | - |
dc.department | İzmir Ekonomi Üniversitesi | en_US |
dc.authorwosid | Akbugday, Burak/Gso-0234-2022 | - |
dc.authorscopusid | 57215310544 | - |
dc.authorscopusid | 57195223021 | - |
dc.authorscopusid | 57211987353 | - |
dc.authorscopusid | 35617283100 | - |
dc.identifier.startpage | 1397 | en_US |
dc.identifier.endpage | 1401 | en_US |
dc.identifier.wos | WOS:001349787000280 | - |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
dc.identifier.scopusquality | N/A | - |
dc.identifier.wosquality | N/A | - |
dc.description.woscitationindex | Conference Proceedings Citation Index - Science | - |
item.openairetype | Conference Object | - |
item.grantfulltext | reserved | - |
item.languageiso639-1 | en | - |
item.cerifentitytype | Publications | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.fulltext | With Fulltext | - |
crisitem.author.dept | 05.06. Electrical and Electronics Engineering | - |
crisitem.author.dept | 05.06. 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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