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dc.contributor.authorBergil E.
dc.contributor.authorBozkurt M.R.
dc.contributor.authorOral C.
dc.date.accessioned2024-03-12T19:35:27Z
dc.date.available2024-03-12T19:35:27Z
dc.date.issued2021
dc.identifier.issn15500594
dc.identifier.urihttps://doi.org/10.1177/1550059420966436
dc.identifier.urihttps://hdl.handle.net/20.500.12450/2914
dc.description.abstractDecreasing the processor load to an acceptable level challenges researchers as an important threshold in the study of real-time detection and the prediction of epileptic seizures. The main methods in overcoming this problem are feature selection, dimension reduction, and electrode selection. This study is an evaluation of the performances of EEG signals, obtained from different channels in the detection processes of epileptic stages, in epileptic individuals. In particular, it aimed to analyze the separation levels of preictal periods from other periods and to evaluate the effects of the electrode selection on seizure prediction studies. The EEG signals belong to 14 epileptic patients. A feature set was formed for each patient using 20 features widely used in epilepsy studies. The number of features was decreased to 8 using principal component analysis. The reduced feature set was divided into testing and training data, using the cross-validation method. The testing data were classified with linear discriminant analysis and the results of the classification were evaluated individually for each patient and channel. Variability of up to 29.48 % was observed in the average of classification accuracy due to the selection of channels. © EEG and Clinical Neuroscience Society (ECNS) 2020.en_US
dc.language.isoengen_US
dc.publisherSAGE Publications Inc.en_US
dc.relation.ispartofClinical EEG and Neuroscienceen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectchannel effecten_US
dc.subjectclassificationen_US
dc.subjectEEG signalen_US
dc.subjectepilepsyen_US
dc.subjectseizure predictionen_US
dc.subjectaccuracyen_US
dc.subjectArticleen_US
dc.subjectchannel effecten_US
dc.subjectclinical articleen_US
dc.subjectdiscriminant analysisen_US
dc.subjectelectroencephalographyen_US
dc.subjectentropyen_US
dc.subjectepilepsyen_US
dc.subjectepileptic patienten_US
dc.subjectfeature selectionen_US
dc.subjecthumanen_US
dc.subjectmachine learningen_US
dc.subjectpredictionen_US
dc.subjectprincipal component analysisen_US
dc.subjectsensitivity and specificityen_US
dc.subjectsignal detectionen_US
dc.subjectsignal processingen_US
dc.subjecttrainingen_US
dc.subjectalgorithmen_US
dc.subjectepilepsyen_US
dc.subjectseizureen_US
dc.subjectAlgorithmsen_US
dc.subjectDiscriminant Analysisen_US
dc.subjectElectroencephalographyen_US
dc.subjectEpilepsyen_US
dc.subjectHumansen_US
dc.subjectPrincipal Component Analysisen_US
dc.subjectSeizuresen_US
dc.titleAn Evaluation of the Channel Effect on Detecting the Preictal Stage in Patients With Epilepsyen_US
dc.typearticleen_US
dc.departmentAmasya Üniversitesien_US
dc.identifier.volume52en_US
dc.identifier.issue5en_US
dc.identifier.startpage376en_US
dc.identifier.endpage385en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopus2-s2.0-85093818613en_US
dc.identifier.doi10.1177/1550059420966436
dc.department-tempBergil, E., Department of Electrical and Electronics Engineering, Faculty of Technology, Amasya University, Amasya, Turkey; Bozkurt, M.R., Department of Electrical and Electronics Engineering, Faculty of Engineering, Sakarya University, Sakarya, Turkey; Oral, C., Department of Electrical and Electronics Engineering, Faculty of Technology, Amasya University, Amasya, Turkeyen_US
dc.authorscopusid36974928900
dc.authorscopusid48761063800
dc.authorscopusid36976051300
dc.identifier.pmid33084398en_US


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