MACHINE LEARNING-BASED STROKE DISEASE DIAGNOSIS USING ELECTROENCEPHALOGRAM (EEG) SIGNALS

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dc.contributor.authorSawan, Aktham f.
dc.contributor.authorAwad, Mohammed
dc.contributor.authorQasrawi, Radwan
dc.contributor.authorSowan, Mohammad
dc.date.accessioned2024-05-19T14:40:10Z
dc.date.available2024-05-19T14:40:10Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractStroke is currently ranked as the third leading cause of death worldwide. While computed tomography (CT) and magnetic resonance imaging (MRI) are commonly used for stroke diagnosis, they have their limitations. CT scans can be time-consuming, taking up to 8 hours to complete diagnosis, while MRI procedures can be lengthy, often making it impractical for most stroke patients. This has led to the necessity of exploring new methods for stroke detection, particularly utilizing EEG signals. In this paper, we propose a cloud computing -based machine learning (ML) system that leverages MUSE2 to diagnose stroke patients by analysing EEG signals. Our dataset, collected from Al Bashir Hospital between 2021 and 2022, consists of a randomly selected sample of 31 stroke patients and 31 healthy individuals. To pre-process the collected dataset, we employ Fourier and wavelet transformations. The processed EEG signals are then transmitted over the Internet to the ML model for stroke diagnosis. Real-time results are delivered to authorized personnel via SMS. During our research, various classifiers were evaluated, and a modified XGboost classifier emerged as the most effective choice. It outperformed other ML classifiers with an impressive accuracy of 96.87%.en_US
dc.identifier.endpage2866en_US
dc.identifier.issn1823-4690
dc.identifier.issue6en_US
dc.identifier.scopus2-s2.0-85184025145en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage2847en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12713/4919
dc.identifier.volume18en_US
dc.identifier.wosWOS:001154412900018en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherTaylors Univ Sdn Bhden_US
dc.relation.ispartofJournal of Engineering Science and Technologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectClouden_US
dc.subjectEegen_US
dc.subjectMachine Learningen_US
dc.subjectMuse2en_US
dc.subjectStrokeen_US
dc.subjectWearable Devicesen_US
dc.titleMACHINE LEARNING-BASED STROKE DISEASE DIAGNOSIS USING ELECTROENCEPHALOGRAM (EEG) SIGNALSen_US
dc.typeArticleen_US

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