1D-local binary pattern based feature extraction for classification of epileptic EEG signals

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dc.authoridTekin, Ramazan/0000-0003-4325-6922
dc.authoridUYAR, Murat/0000-0001-7243-7939
dc.contributor.authorKaya, Yilmaz
dc.contributor.authorUyar, Murat
dc.contributor.authorTekin, Ramazan
dc.contributor.authorYildirim, Selcuk
dc.date.accessioned2024-12-24T19:25:19Z
dc.date.available2024-12-24T19:25:19Z
dc.date.issued2014
dc.departmentSiirt Üniversitesi
dc.description.abstractIn this paper, an effective approach for the feature extraction of raw Electroencephalogram (EEG) signals by means of one-dimensional local binary pattern (1D-LBP) was presented. For the importance of making the right decision, the proposed method was performed to be able to get better features of the EEG signals. The proposed method was consisted of two stages: feature extraction by 1D-LBP and classification by classifier algorithms with features extracted. On the classification stage, the several machine learning methods were employed to uniform and non-uniform 1D-LBP features. The proposed method was also compared with other existing techniques in the literature to find out benchmark for an epileptic data set. The implementation results showed that the proposed technique could acquire high accuracy in classification of epileptic EEG signals. Also, the present paper is an attempt to develop a general-purpose feature extraction scheme, which can be utilized to extract features from different categories of EEG signals. (C) 2014 Elsevier Inc. All rights reserved.
dc.identifier.doi10.1016/j.amc.2014.05.128
dc.identifier.endpage219
dc.identifier.issn0096-3003
dc.identifier.issn1873-5649
dc.identifier.scopus2-s2.0-84903136102
dc.identifier.scopusqualityQ1
dc.identifier.startpage209
dc.identifier.urihttps://doi.org/10.1016/j.amc.2014.05.128
dc.identifier.urihttps://hdl.handle.net/20.500.12604/6348
dc.identifier.volume243
dc.identifier.wosWOS:000340563800020
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier Science Inc
dc.relation.ispartofApplied Mathematics and Computation
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241222
dc.subject1D-local binary patterns
dc.subjectEpilepsy
dc.subjectEEG classification
dc.subjectFeature extraction
dc.title1D-local binary pattern based feature extraction for classification of epileptic EEG signals
dc.typeArticle

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