Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14365/5614
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Sen, Sena Yagmur | - |
dc.contributor.author | Akan, Aydin | - |
dc.contributor.author | Cura, Ozlem Karabiber | - |
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/EUSIPCO63174.2024.10715005 | - |
dc.description.abstract | This paper presents a novel early-stage Alzheimer's dementia (AD) disease detection based on convolutional neural networks (CNNs). As it is widely used in detection and classification of AD disease, a time-frequency (TF) method has been proposed for AD detection. It has been described to address the problem of detecting early-stage AD by combining TF and CNN methods. The method is developed by utilizing the well-known structural similarity index measure (SSIM) to obtain discriminative features in each TF image. Experimental results demonstrate that the proposed method outperforms the early-stage AD detection using advanced signal decomposition algorithm that is intrinsic time-scale decomposition (ITD), and it achieves a notable improvement in terms of the detection success rates compared to AD detection from TF images of raw EEG signals. | 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 | Electroencephalography (Eeg) | en_US |
dc.subject | Intrinsic Time-Scale Decomposition (Itd) | en_US |
dc.subject | Short-Time Fourier Transform (Stft) | en_US |
dc.subject | Convolutional Neural Network (Cnn) | en_US |
dc.title | Alzheimer's Dementia Detection: an Optimized Approach Using Itd of Eeg Signals | en_US |
dc.type | Conference Object | en_US |
dc.identifier.doi | 10.23919/EUSIPCO63174.2024.10715005 | - |
dc.identifier.scopus | 2-s2.0-85208442090 | - |
dc.department | İzmir Ekonomi Üniversitesi | en_US |
dc.authorwosid | Sen, Sena Yagmur/Iup-8865-2023 | - |
dc.authorscopusid | 57215314563 | - |
dc.authorscopusid | 35617283100 | - |
dc.authorscopusid | 57195223021 | - |
dc.identifier.startpage | 1377 | en_US |
dc.identifier.endpage | 1381 | en_US |
dc.identifier.wos | WOS:001349787000276 | - |
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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