A Deep Learning Approach to Alzheimer's Diagnosis Using EEG Data: Dual-Attention and Optuna-Optimized SVM

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dc.contributor.authorArikan, Funda Bulut
dc.contributor.authorCetintas, Dilber
dc.contributor.authorAksoy, Aziz
dc.contributor.authorYildirim, Muhammed
dc.date.accessioned2026-06-19T06:37:52Z
dc.date.available2026-06-19T06:37:52Z
dc.date.issued2025
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractBackground/Objectives: Alzheimer's disease (AD) is a progressive neurodegenerative disorder, pathologically defined by the accumulation of amyloid-beta plaques and tau-related neurofibrillary tangles in the brain. It represents a principal driver of cognitive deterioration in middle-aged and elderly populations. Early diagnosis and pharmacological management of the disease markedly improve both the quality and duration of life. Methods: Electroencephalography (EEG) is critical in detecting and analyzing Alzheimer's disease. The widespread use of mobile EEG devices in recent years has necessitated real-time and effective data processing. However, extracting disease-specific features from EEG data still poses a significant challenge, especially in cases that must be completed quickly. This study aims to determine the frequency bands associated with Alzheimer's disease in EEG data obtained from multiple channels and to accelerate the detection methods. An accurate classification that requires little computation is the primary goal. Results: EEG recordings of 48 individuals (24 AD and 24 healthy controls (HC)) obtained from Florida State University were divided into Alpha, Beta, Delta, Gamma, and Theta frequency bands; scalograms and spectrograms were generated for each frequency band. The effectiveness of these bands was evaluated using the MobileNetV2 architecture. The results showed that Delta and Beta frequency bands were the most significant for Alzheimer's detection. By analyzing the features obtained from the Delta and Beta bands using the MobileNetV2 model integrated with the Dual-Attention Mechanism, it was determined that the attention mechanisms improved model performance by 2%. In addition, the use of an SVM classifier with hyperparameters optimized via Optuna resulted in approximately 3% performance improvement, suggesting that hyperparameter tuning may contribute positively to classification accuracy. Furthermore, combining features obtained from these frequency bands increased the detection performance when evaluated with larger datasets. Conclusions: The study demonstrates the potential of frequency band-based analyses and feature fusion methods to increase the accuracy and efficiency of Alzheimer's diagnosis using EEG data. The results are promising; however, they should be interpreted with caution regarding their generalizability.
dc.identifier.doi10.3390/biomedicines13082017
dc.identifier.issn2227-9059
dc.identifier.issue8
dc.identifier.orcid0000-0003-0710-2280
dc.identifier.orcid0000-0003-1866-4721
dc.identifier.orcid0000-0002-9683-6691
dc.identifier.pmid40868268
dc.identifier.scopus2-s2.0-105014502213
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/biomedicines13082017
dc.identifier.urihttps://hdl.handle.net/20.500.12899/5255
dc.identifier.volume13
dc.identifier.wosWOS:001557905000001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofBiomedicines
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260612
dc.subjectAlzheimer
dc.subjectArtificial Intelligence
dc.subjectDeep Learning
dc.subjectEeg
dc.subjectMobilenetv2
dc.titleA Deep Learning Approach to Alzheimer's Diagnosis Using EEG Data: Dual-Attention and Optuna-Optimized SVM
dc.typeArticle

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