Epileptic seizure detection based on new hybrid models with electroencephalogram signals

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dc.authorid0000-0003-1840-9958
dc.contributor.authorPolat, Kemal
dc.contributor.authorNour, Majid
dc.date.accessioned2021-06-23T19:53:49Z
dc.date.available2021-06-23T19:53:49Z
dc.date.issued2020
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractObjectives: Epileptic seizures are one of the most common diseases in society and difficult to detect. In this study, a new method was proposed to automatically detect and classify epileptic seizures from EEG (Electroencephalography) signals. Methods: In the proposed method, EEG signals classification five-classes including the cases of eyes open, eyes closed, healthy, from the tumor region, an epileptic seizure, has been carried out by using the support vector machine (SVM) and the normalization methods comprising the z-score, minimum-maximum, and MAD normalizations. To classify the EEG signals, the support vector machine classifiers having different kernel functions, including Linear, Cubic, and Medium Gaussian, have been used. In order to evaluate the performance of the proposed hybrid models, the confusion matrix, ROC curves, and classification accuracy have been used. The used SVM models are Linear SVM, Cubic SVM, and Medium Gaussian SVM. Results: Without the normalizations, the obtained classification accuracies are 76.90%, 82.40%, and 81.70% using Linear SVM, Cubic SVM, and Medium Gaussian SVM, respectively. After applying the z-score normalization to the multi-class EEG signals dataset, the obtained classification accuracies are 77.10%, 82.30%, and 81.70% using Linear SVM, Cubic SVM, and Medium Gaussian SVM, respectively. With the minimum-maximum normalization, the obtained classification accuracies are 77.20%, 82.40%, and 81.50% using Linear SVM, Cubic SVM, and Medium Gaussian SVM, respectively. Moreover, finally, after applying the MAD normalization to the multi-class EEG signals dataset, the obtained classification accuracies are 76.70%, 82.50%, and 81.40% using Linear SVM, Cubic SVM, and Medium Gaussian SVM, respectively. Conclusion: The obtained results have shown that the best hybrid model is the combination of cubic SVM and MAD normalization in the classification of EEG signals classification five-classes. (C) 2020 AGBM. Published by Elsevier Masson SAS. All rights reserved.en_US
dc.identifier.doi10.1016/j.irbm.2020.06.008
dc.identifier.endpage353en_US
dc.identifier.issn1959-0318
dc.identifier.issn1876-0988
dc.identifier.issue6en_US
dc.identifier.scopus2-s2.0-85087744908en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage331en_US
dc.identifier.urihttps://doi.org/10.1016/j.irbm.2020.06.008
dc.identifier.urihttps://hdl.handle.net/20.500.12491/10276
dc.identifier.volume41en_US
dc.identifier.wosWOS:000591924600005en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherElsevier Science Incen_US
dc.relation.ispartofIrbmen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectElectroencephalographyen_US
dc.subjectMulti-class Classificationen_US
dc.subjectSupport Vector Machineen_US
dc.subjectNormalizationen_US
dc.subjectHybrid Modelsen_US
dc.titleEpileptic seizure detection based on new hybrid models with electroencephalogram signalsen_US
dc.typeArticleen_US

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