Epilepsy diagnosis using artificial neural network learned by PSO

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dc.contributor.authorYalcin, Nesibe
dc.contributor.authorTezel, Gulay
dc.contributor.authorKarakuzu, Cihan
dc.date.accessioned2020-03-26T19:05:56Z
dc.date.available2020-03-26T19:05:56Z
dc.date.issued2015
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractElectroencephalogram (EEG) is used routinely for diagnosis of diseases occurring in the brain. It is a very useful clinical tool in the classification of epileptic seizures and the diagnosis of epilepsy. In this study, epilepsy diagnosis has been investigated using EEG records. For this purpose, an artificial neural network (ANN), widely used and known as an active classification technique, is applied. The particle swarm optimization (PSO) method, which does not need gradient calculation, derivative information, or any solution of differential equations, is preferred as the training algorithm for the ANN. A PSO-based neural network (PSONN) model is diversified according to PSO versions, and 7 PSO-based neural network models are described. Among these models, PSONN3 and PSONN4 are determined to be appropriate models for epilepsy diagnosis due to having better classification accuracy. The training methods-based PSO versions are compared with the backpropagation algorithm, which is a traditional method. In addition, different numbers of neurons, iterations/generations, and swarm sizes have been considered and tried. Results obtained from the models are evaluated, interpreted, and compared with the results of earlier works done with the same dataset in the literature.en_US
dc.identifier.doi10.3906/elk-1212-151en_US
dc.identifier.endpage432en_US
dc.identifier.issn1300-0632en_US
dc.identifier.issn1303-6203en_US
dc.identifier.issue2en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage421en_US
dc.identifier.urihttps://dx.doi.org/10.3906/elk-1212-151
dc.identifier.urihttps://hdl.handle.net/20.500.12395/32114
dc.identifier.volume23en_US
dc.identifier.wosWOS:000349678400007en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherTUBITAK SCIENTIFIC & TECHNICAL 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/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectArtificial neural networksen_US
dc.subjectbackpropagation algorithmen_US
dc.subjectelectroencephalogramen_US
dc.subjectepilepsy diagnosisen_US
dc.subjectparticle swarm optimizationen_US
dc.titleEpilepsy diagnosis using artificial neural network learned by PSOen_US
dc.typeArticleen_US

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