Detection of Alzheimer's disease from EEG signals using explainable artificial intelligence analysis

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dc.authorid0009-0002-4501-1662
dc.authorid0000-0002-8061-8059
dc.authorid0000-0003-1840-9958
dc.authorscopusid58974605500
dc.authorscopusid57214823678
dc.authorscopusid8945093900
dc.contributor.authorArabacı, Bahadır
dc.contributor.authorÖcal, Hakan
dc.contributor.authorPolat, Kemal
dc.date.accessioned2024-09-25T19:42:52Z
dc.date.available2024-09-25T19:42:52Z
dc.date.issued2024
dc.departmentBAİBÜ, Lisansüstü Eğitim Enstitüsü, Fen Bilimleri, Elektrik Elektronik Mühendisliği Ana Bilim Dalıen_US
dc.descriptionBerdan Civata B.C.; et al.; Figes; Koluman; Loodos; Tarsus Universityen_US
dc.description32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024 -- 15 May 2024 through 18 May 2024 -- Mersin -- 201235en_US
dc.description.abstractIn this study, the evaluation of classification models with frequency and chaotic features was aimed for the classification of healthy individuals and Alzheimer's patients using EEG signals. Morlet wavelet transform was employed for calculating EEG features to determine the characteristics in the frequency domain. Additionally, Lyapunov exponents were utilized for the analysis of chaotic features, and significant EEG channels were identified from the obtained results of the wavelet transform. Using permutation importance, the impact of each feature on the performance of the classification model was assessed. In this evaluation, the Random Forest model stood out in overall performance, showing the highest accuracy (0.7614), precision (0.7546), and F1 score (0.793) compared to other models. Furthermore, the Naive Bayes model achieved the highest sensitivity (0.8662) in detecting positive instances. © 2024 IEEE.en_US
dc.identifier.doi10.1109/SIU61531.2024.10600949
dc.identifier.endpage4
dc.identifier.isbn979-835038896-1
dc.identifier.scopus2-s2.0-85200897216en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage1
dc.identifier.urihttps://doi.org/10.1109/SIU61531.2024.10600949
dc.identifier.urihttps://hdl.handle.net/20.500.12491/12323
dc.indekslendigikaynakScopusen_US
dc.institutionauthorArabacı, Bahadır
dc.institutionauthorPolat, Kemal
dc.institutionauthorid0009-0002-4501-1662
dc.institutionauthorid0000-0003-1840-9958
dc.language.isotren_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024 - Proceedingsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzYK_20240925en_US
dc.subjectAlzheimer's Diseaseen_US
dc.subjectClassificationen_US
dc.subjectElectroencephalographyen_US
dc.subjectMachine Learningen_US
dc.titleDetection of Alzheimer's disease from EEG signals using explainable artificial intelligence analysisen_US
dc.title.alternativeAçıklanabilir yapay zeka analizi kullanılarak EEG sinyallerinden Alzheimer hastalığı tespitien_US
dc.typeConference Objecten_US

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