Automated Recognition of Epilepsy from EEG Signals

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dc.contributor.authorYildirim, Mehmet
dc.contributor.authorYildiz, Abdulnasir
dc.date.accessioned2024-04-24T17:33:16Z
dc.date.available2024-04-24T17:33:16Z
dc.date.issued2017
dc.departmentDicle Üniversitesien_US
dc.description2017 International Artificial Intelligence and Data Processing Symposium (IDAP) -- SEP 16-17, 2017 -- Malatya, TURKEYen_US
dc.description.abstractIn this study, it is aimed to design an automatic pattern recognition system for the detection of epilepsy which distinguishes healthy and seizure electroencephalography (EEG) signals. During the study, 100 EEG signals from patients were used during the opened eyes and healthy epileptic seizures. Each EEG signal consisting of 4096 samples was divided into 256 samples and a total of 3200 signals were obtained. The designed pattern recognition system has been developed in 3 basic parts. In the first part, the power spectral density (PSD) estimation is performed with the periodogram and Welch methods and the frequency domain information of the EEG signals is obtained. In the second part, the feature vectors are found from the frequency domain information obtained in the periodogram and Welch PSD estimation. In the third part, healthy EEG signals from the eigenvectors obtained by using K-Nearest Neighbor Algorithm (K-NN) and Support Vector Machine (SVM) classifiers are distinguished from pathological EEG signals. 5-fold cross-validation method was used in evaluating the accuracy performance of the designed system. The total classification accuracy of the system was found to be 99.66% with K-NN, 99.72% with SVM for periodogram PSD estimation and 99.72% with K-NN, 99.75% with SVM for Welch PSD estimation. The results of the pattern recognition system designed in the study are promising because they are close to the work done with different approaches in the literature. The pattern recognition system designed here is not a diagnostic tool. It is foreseen that physicians may be useful in evaluating preliminary diagnosis.en_US
dc.description.sponsorshipIEEE Turkey Sect,Anatolian Scien_US
dc.identifier.isbn978-1-5386-1880-6
dc.identifier.scopus2-s2.0-85039910057
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://hdl.handle.net/11468/20581
dc.identifier.wosWOS:000426868700094
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isotren_US
dc.publisherIeeeen_US
dc.relation.ispartof2017 International Artificial Intelligence and Data Processing Symposium (Idap)
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectEpilepsyen_US
dc.subjectElektroensefalografien_US
dc.subjectPower Spectral Densityen_US
dc.subjectK Nearest Neighbor Algorithmen_US
dc.subjectSupport Vector Machineen_US
dc.titleAutomated Recognition of Epilepsy from EEG Signalsen_US
dc.titleAutomated Recognition of Epilepsy from EEG Signals
dc.typeConference Objecten_US

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