A novel hybrid model in the diagnosis and classification of Alzheimer's disease using EEG signals: Deep ensemble learning (DEL) approach

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dc.contributor.authorNour, Majid
dc.contributor.authorSenturk, Umit
dc.contributor.authorPolat, Kemal
dc.date.accessioned2024-09-25T19:56:14Z
dc.date.available2024-09-25T19:56:14Z
dc.date.issued2024
dc.departmentAbant İzzet Baysal Üniversitesien_US
dc.description.abstractRecent years have witnessed a surge of sophisticated computer-aided diagnosis techniques involving Artificial Intelligence (AI) to accurately diagnose and classify Alzheimer's disease (AD) and other forms of Dementia. Despite these advancements, there is still a lack of reliable and accurate methods for distinguishing between (AD) and Healthy Controls (HC) using Electroencephalography signals (EEG). The main challenge is finding the right features from the intricate spectral-temporal EEG data, which can provide information sufficient for diagnosis. This study proposes a new approach integrating Deep Ensemble Learning (DEL) and 2-dimensional Convolutional Neural Networks (2D-CNN) to address these issues. Combining state-of-the-art supervised deep learning algorithms within an ensemble model architecture aims to accurately diagnose and classify EEG signals of AD and HC subjects. Public EEG-based Alzheimer's datasets have been classified in the DEL model without applying any feature extraction after cleaning from noise and artifacts. Furthermore, the proposed DEL model used 5 different 2D-CNN models as internal classifiers. As a result, the EEG-based DEL model proposed for the first time provided high accuracy in AD classification. The proposed DEL model reached an average accuracy of 97.9% in AD classification due to 5 cross-fold training. In conclusion, this work renders that incorporating ensemble learning techniques into automotive health applications create extensible and stable AI models needed for computer-aided diagnostic. However, although the reported results and evaluation are promising, further efforts will need to be made to improve the accuracy of our proposed model. In addition, a fine-grid evaluation will be necessary to accurately understand potential impacts in clinical applications, such as earlier diagnosis or treatment decisions.en_US
dc.description.sponsorshipInstitutional Fund Projects (IFPIP) [1038-135-1443]; Ministry of Education and King Abdulaziz University, DSR, Jeddah, Saudi Arabiaen_US
dc.description.sponsorshipThis research work was funded by Institutional Fund Projects under grant No. (IFPIP: 1038-135-1443) . The authors gratefully acknowledge technical and financial support provided by the Ministry of Education and King Abdulaziz University, DSR, Jeddah, Saudi Arabia.en_US
dc.identifier.doi10.1016/j.bspc.2023.105751
dc.identifier.issn1746-8094
dc.identifier.issn1746-8108
dc.identifier.scopus2-s2.0-85177212326en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.bspc.2023.105751
dc.identifier.urihttps://hdl.handle.net/20.500.12491/13203
dc.identifier.volume89en_US
dc.identifier.wosWOS:001115927500001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofBiomedical Signal Processing And Controlen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzYK_20240925en_US
dc.subjectAlzheimer 's disease classificationen_US
dc.subjectDeep ensemble learningen_US
dc.subjectEEGen_US
dc.subjectDementiaen_US
dc.subjectExplainabilityen_US
dc.titleA novel hybrid model in the diagnosis and classification of Alzheimer's disease using EEG signals: Deep ensemble learning (DEL) approachen_US
dc.typeArticleen_US

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