Exploring Rhythms and Channels-Based EEG Biomarkers for Early Detection of Alzheimer's Disease

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dc.contributor.authorSiuly, Siuly
dc.contributor.authorAlcin, Omer Faruk
dc.contributor.authorWang, Hua
dc.contributor.authorLi, Yan
dc.contributor.authorWen, Peng
dc.date.accessioned2026-06-19T06:39:22Z
dc.date.available2026-06-19T06:39:22Z
dc.date.issued2024
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractThere is no treatment that permanently cures Alzheimer's disease (AD); however, early detection can alleviate the severe effects of the disease. To support early detection of the different stages of AD (e.g., mild, moderate), the key aim of this study is to develop a computer aided diagnostic (CAD) framework that include a long short-term memory (LSTM) network using massive multi-channel electroencephalogram (EEG) data. Although EEG rhythms and EEG channels jointly possess important biomarkers that may be used for diagnosis of AD, but the traditional methods did not explore this issue in any research. To address this problem, this study introduces a new framework to identify the optimal EEG rhythms and channels required for the diagnosis of AD. The proposed framework was tested on a real-time AD EEG dataset. The results reveal that together, the gamma and beta rhythms in the channels, Cz, F4, P4, T6, Pz were the most reliable biomarker for identifying AD and the proposed LSTM based model yielded the best performance. Additionally, another mild cognitive impairment (MCI) EEG dataset was used to test the proposed approach, and the results were excellent (accuracy>99%). The proposed framework will be useful for creating a CAD system to perform automatic AD diagnosis.
dc.identifier.doi10.1109/TETCI.2024.3353610
dc.identifier.endpage1623
dc.identifier.issn2471-285X
dc.identifier.issue2
dc.identifier.orcid0000-0002-2917-3736
dc.identifier.orcid0000-0002-4694-4926
dc.identifier.orcid0000-0003-2491-0546
dc.identifier.orcid0000-0002-8465-0996
dc.identifier.orcid0000-0003-0939-9145
dc.identifier.scopus2-s2.0-85183626454
dc.identifier.scopusqualityN/A
dc.identifier.startpage1609
dc.identifier.urihttps://doi.org/10.1109/TETCI.2024.3353610
dc.identifier.urihttps://hdl.handle.net/20.500.12899/5575
dc.identifier.volume8
dc.identifier.wosWOS:001167529800001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIeee-Inst Electrical Electronics Engineers Inc
dc.relation.ispartofIeee Transactions on Emerging Topics in Computational Intelligence
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20260612
dc.subjectAlzheimer'S Disease (Ad)
dc.subjectElectroencephalography (Eeg)
dc.subjectBiomarkers Of Eeg
dc.subjectLong Short-Term Memory (Lstm)
dc.subjectMild Cognitive Impairment (Mci)
dc.subjectFeature Extraction
dc.subjectClassification
dc.titleExploring Rhythms and Channels-Based EEG Biomarkers for Early Detection of Alzheimer's Disease
dc.typeArticle

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