Classification of resting-state status based on sample entropy and power spectrum of electroencephalography (EEG)

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dc.contributor.authorMohamed, Ahmed M. A.
dc.contributor.authorUçan, Osman Nuri
dc.contributor.authorBayat, Oğuz
dc.contributor.authorDuru, Adil Deniz
dc.date.accessioned2021-05-15T11:33:25Z
dc.date.available2021-05-15T11:33:25Z
dc.date.issued2020
dc.departmentMühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractAn electroencephalogram (EEG) is a significant source of diagnosing brain issues. It is also a mediator between the external world and the brain, especially in the case of any mental illness; however, it has been widely used to monitor the dynamics of the brain in healthy subjects. This paper discusses the resting state of the brain with eyes open (EO) and eyes closed (EC) by using sixteen channels by the use of conventional frequency bands and entropy of the EEG signal. The Fast Fourier Transform (FFT) and sample entropy (SE) of each sensor are computed as methods of feature extraction. Six classifiers, including logistic regression (LR), K-Nearest Neighbors (KNN), linear discriminant (LD), decision tree (DT), support vector machine (SVM), and Gaussian Naive Bayes (GNB) are used to discriminate the resting states of the brain based on the extracted features. EEG data were epoched with one-second-length windows, and they were used to compute the features to classify EO and EC conditions. Results showed that the LR and SVM classifiers had the highest average classification accuracy (97%). Accuracies of LD, KNN, and DT were 95%, 93%, and 92%, respectively. GNB gained the least accuracy (86%) when conventional frequency bands were used. On the other hand, when SE was used, the average accuracies of SVM, LD, LR, GNB, KNN, and DT algorithms were 92% 90%, 89%, 89%, 86%, and 86%, respectively.en_US
dc.identifier.doi10.1155/2020/8853238
dc.identifier.issn1176-2322
dc.identifier.issn1754-2103
dc.identifier.pmid33224269
dc.identifier.scopus2-s2.0-85096583607
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1155/2020/8853238
dc.identifier.urihttps://hdl.handle.net/20.500.12939/154
dc.identifier.volume2020en_US
dc.identifier.wosWOS:000593491800001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorMohamed, Ahmed M. A.
dc.institutionauthorUçan, Osman Nuri
dc.institutionauthorBayat, Oğuz
dc.language.isoen
dc.publisherHindawi Ltden_US
dc.relation.ispartofApplied Bionics and Biomechanics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectEEGen_US
dc.subjectSWLDAen_US
dc.subjectEOen_US
dc.titleClassification of resting-state status based on sample entropy and power spectrum of electroencephalography (EEG)
dc.typeArticle

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