Complexity of EEG dynamics for early diagnosis of alzheimer's disease using permutation entropy neuromarker

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dc.authorid0000-0001-9245-6790en_US
dc.authorid0000-0002-9368-8902en_US
dc.contributor.authorŞeker, Mesut
dc.contributor.authorÖzbek, Yağmur
dc.contributor.authorYener, Görsev
dc.contributor.authorÖzerdem, Mehmet Siraç
dc.date.accessioned2024-02-20T06:38:01Z
dc.date.available2024-02-20T06:38:01Z
dc.date.issued2021en_US
dc.departmentDicle Üniversitesi, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractBackground and objective Electroencephalogram (EEG) is one of the most demanded screening tools that investigates the effects of Alzheimer's Disease (AD) on human brain. Identification of AD in early stage gives rise to efficient treatment in dementia. Mild Cognitive Impairment (MCI) is considered as a conversion stage. Reducing EEG complexity can be used as a marker to detect AD. The aim of this study is to develop a 3-way diagnostic classification using EEG complexity in the detection of MCI/AD in clinical practice. This study also investigates the effects of different eyes states, i.e. eyes-open, eyes-closed on classification performance. Methods EEG recordings from 85 AD, 85 MCI subjects, and 85 Healthy Controls with eyes-open and eyes- closed are analyzed. Permutation Entropy (PE) values are computed from frontal, central, parietal, temporal, and occipital regions for each EEG epoch. Distribution of PE values are visualized to observe discrimination of MCI/AD with HC. Visual investigations are combined with statistical analysis using ANOVA to determine whether groups are significant or not. Multinomial Logistic Regression model is applied to feature sets in order to classify participants individually. Results Distribution of measured PE shows that EEG complexity is lower in AD and higher in HC group. MCI group is observed as an intermediate form due to heterogeneous values. Results from 3-way classification indicate that F1-scores and rates of sensitivity and specificity achieve the highest overall discrimination rates reaching up to 100% for at TP8 for eyes-closed condition; and C3, C4, T8, O2 electrodes for eyes-open condition. Classification of HC from both patient groups is achieved best. Eyes-open state increases discrimination of MCI and AD. Conclusions This nonlinear EEG methodology study contributes to literature with high discrimination rates for identification of AD. PE is recommended as a practical diagnostic neuro-marker for AD studies. Resting state EEG at eyes-open condition can be more advantageous over eyes-closed EEG recordings for diagnosis of AD.en_US
dc.identifier.citationŞeker, M., Özbek, Y., Yener, G. ve Özerdem, M.S. (2021). Complexity of EEG dynamics for early diagnosis of alzheimer's disease using permutation entropy neuromarker. Computer Methods and Programs in Biomedicine, 206, 106116en_US
dc.identifier.doi10.1016/j.cmpb.2021.106116
dc.identifier.endpage13en_US
dc.identifier.issn1872-7565
dc.identifier.pmid33957376
dc.identifier.scopus2-s2.0-85105107238
dc.identifier.scopusqualityQ1
dc.identifier.startpage1en_US
dc.identifier.urihttps://hdl.handle.net/11468/13352
dc.identifier.volume206en_US
dc.identifier.wosWOS:000663415100011
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorŞeker, Mesut
dc.institutionauthorÖzerdem, Mehmet Siraç
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofComputer Methods and Programs in Biomedicine
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.subjectAlzheimeren_US
dc.subjectMild cognitive impairmenten_US
dc.subjectDementiaen_US
dc.subjectEEGen_US
dc.subjectEntropyen_US
dc.subjectDiagnosisen_US
dc.subjectBiomarkeren_US
dc.titleComplexity of EEG dynamics for early diagnosis of alzheimer's disease using permutation entropy neuromarkeren_US
dc.titleComplexity of EEG dynamics for early diagnosis of alzheimer's disease using permutation entropy neuromarker
dc.typeArticleen_US

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