A New Framework for Automatic Detection of Patients With Mild Cognitive Impairment Using Resting-State EEG Signals

dc.authorid0000-0002-2917-3736en_US
dc.contributor.authorSiuly, Siuly
dc.contributor.authorAlçin, Ömer Faruk
dc.contributor.authorKabir, Enamul
dc.contributor.authorŞengür, Abdulkadir
dc.contributor.authorWang, Hua
dc.contributor.authorZhang, Yanchun
dc.contributor.authorWhittaker, Frank
dc.date.accessioned2021-09-16T18:20:30Z
dc.date.available2021-09-16T18:20:30Z
dc.date.issued2020en_US
dc.departmentMTÖ Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractMild cognitive impairment (MCI) can be an indicator representing the early stage of Alzheimier's disease (AD). AD, which is the most common form of dementia, is a major public health problem worldwide. Efficient detection of MCI is essential to identify the risks of AD and dementia. Currently Electroencephalography (EEG) is the most popular tool to investigate the presenence of MCI biomarkers. This study aims to develop a new framework that can use EEG data to automatically distinguish MCI patients from healthy control subjects. The proposed framework consists of noise removal (baseline drift and power line interference noises), segmentation, data compression, feature extraction, classification, and performance evaluation. This study introduces Piecewise Aggregate Approximation (PAA) for compressing massive volumes of EEG data for reliable analysis. Permutation entropy (PE) and auto-regressive (AR) model features are investigated to explore whether the changes in EEG signals can effectively distinguish MCI from healthy control subjects. Finally, three models are developed based on three modern machine learning techniques: Extreme Learning Machine (ELM); Support Vector Machine (SVM) and K-Nearest Neighbours (KNN) for the obtained feature sets. Our developed models are tested on a publicly available MCI EEG database and the robustness of our models is evaluated by using a 10-fold cross validation method. The results show that the proposed ELM based method achieves the highest classification accuracy (98.78%) with lower execution time (0.281 seconds) and also outperforms the existing methods. The experimental results suggest that our proposed framework could provide a robust biomarker for efficient detection of MCI patients.en_US
dc.identifier.citationSiuly, S., Alçin, Ö. F., Kabir, E., Şengür, A., Wang, H., Zhang, Y., & Whittaker, F. (2020). A new framework for automatic detection of patients with mild cognitive impairment using resting-state EEG signals. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28(9), 1966-1976.en_US
dc.identifier.doi10.1109/TNSRE.2020.3013429
dc.identifier.endpage1976en_US
dc.identifier.issn1534-4320en_US
dc.identifier.issn1558-0210en_US
dc.identifier.issue9en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1966en_US
dc.identifier.urihttps://doi.org/10.1109/TNSRE.2020.3013429
dc.identifier.urihttps://hdl.handle.net/20.500.12899/421
dc.identifier.volume28en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorAlçin, Ömer Faruk
dc.language.isoenen_US
dc.publisherIEEE (Institute of Electrical and Electronics Engineers)en_US
dc.relation.ispartofIEEE Transactions on Neural Systems and Rehabilitation Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMild cognitive impairment (MCI)en_US
dc.subjectAlzheimer’s disease (AD)en_US
dc.subjectElectroencephalogram (EEG)en_US
dc.subjectPiecewise aggregate approximation (PAA)en_US
dc.subjectAuto-regressive (AR) modelen_US
dc.subjectPermutation entropy (PE)en_US
dc.subjectExtreme learning machine (ELM)en_US
dc.titleA New Framework for Automatic Detection of Patients With Mild Cognitive Impairment Using Resting-State EEG Signalsen_US
dc.typeArticleen_US

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