Novel approaches for automated epileptic diagnosis using FCBF selection and classification algorithms

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dc.authoridSEN, BAHA/0000-0003-3577-2548
dc.contributor.authorSen, Baha
dc.contributor.authorPeker, Musa
dc.date.accessioned2024-09-29T16:08:18Z
dc.date.available2024-09-29T16:08:18Z
dc.date.issued2013
dc.departmentKarabük Üniversitesien_US
dc.description.abstractThis paper presents a new application for automated epileptic detection using the fast correlation-based feature (FCBF) selection and classification algorithms. This study consists of 3 stages: feature extraction, feature selection from electroencephalography (EEG) signals, and the classification of these signals. In the feature extraction phase, 16 attribute algorithms are used in 5 categories, and 36 feature parameters are obtained from these algorithms. In the feature selection phase, the FCBF algorithm is chosen to select a set of attributes that best represent the EEG signals. The resulting attributes are used as input parameters for the classification algorithms. In the classification phase, the problem is classified with 6 different classification algorithms. The results obtained with the different classification algorithms are provided in order to compare the calculation times and the accuracy rates. The evolution of the proposed system is conducted using k-fold cross-validation, classification accuracy, sensitivity and specificity values, and a confusion matrix. The proposed approach enables 100% classification accuracy with the use of the multilayer perceptron neural network and naive Bayes algorithm. The stated results show that the proposed method is capable of designing a new intelligent assistance diagnostic system.en_US
dc.identifier.doi10.3906/elk-1203-9
dc.identifier.endpage2109en_US
dc.identifier.issn1300-0632
dc.identifier.issn1303-6203
dc.identifier.scopus2-s2.0-84889570242en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage2092en_US
dc.identifier.urihttps://doi.org/10.3906/elk-1203-9
dc.identifier.urihttps://hdl.handle.net/20.500.14619/7482
dc.identifier.volume21en_US
dc.identifier.wosWOS:000326161300020en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherTubitak Scientific & Technological Research Council Turkeyen_US
dc.relation.ispartofTurkish Journal of Electrical Engineering and Computer Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectEEG signalsen_US
dc.subjectclassification algorithmsen_US
dc.subjectFCBF selection algorithmen_US
dc.subjectepilepsy diseaseen_US
dc.titleNovel approaches for automated epileptic diagnosis using FCBF selection and classification algorithmsen_US
dc.typeArticleen_US

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