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dc.contributor.authorGöker, İmranen_US
dc.contributor.authorOsman, Onuren_US
dc.contributor.authorÖzekes, Serhaten_US
dc.contributor.authorBaslo, Mehmet Barışen_US
dc.contributor.authorErtaş, Mustafaen_US
dc.contributor.authorÜlgen, Yektaen_US
dc.date.accessioned2016-05-10T08:27:59Z
dc.date.available2016-05-10T08:27:59Z
dc.date.issued2012
dc.identifier.citationGöker, İ., Osman, O., Özekes, S., Baslo, M. B., Ertaş, M., Ülgen, Y. (2012). Classification of juvenile myoclonic epilepsy data acquired through scanning electromyography with machine learning algorithms. Journal of Medical Systems. 36.5, 2705–2711.en_US
dc.identifier.issn01485598
dc.identifier.issn1573689X
dc.identifier.urihttp://hdl.handle.net/20.500.12294/424
dc.identifier.urihttp://dx.doi.org/10.1007/s10916-011-9746-6
dc.descriptionOsman, Onur (Arel Author), Özekes, Serhat (Arel Author)en_US
dc.description.abstractIn this paper, classification of Juvenile Myoclonic Epilepsy (JME) patients and healthy volunteers included into Normal Control (NC) groups was established using Feed-Forward Neural Networks (NN), Support Vector Machines (SVM), Decision Trees (DT), and Na < ve Bayes (NB) methods by utilizing the data obtained through the scanning EMG method used in a clinical study. An experimental setup was built for this purpose. 105 motor units were measured. 44 of them belonged to JME group consisting of 9 patients and 61 of them belonged to NC group comprising ten healthy volunteers. k-fold cross validation was applied to train and test the models. ROC curves were drawn for k values of 4, 6, 8 and 10. 100% of detection sensitivity was obtained for DT, NN, and NB classification methods. The lowest FP number, which was obtained by NN, was 5.en_US
dc.language.isoengen_US
dc.publisherSpringeren_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectScanning Electromyographyen_US
dc.subjectJuvenile Myoclonic Epilepsyen_US
dc.subjectFeed-Forward Neural Networksen_US
dc.subjectSupport Vector Machinesen_US
dc.subjectDecision Treesen_US
dc.subjectNaïve Bayesen_US
dc.titleClassification of juvenile myoclonic epilepsy data acquired through scanning electromyography with machine learning algorithmsen_US
dc.typearticleen_US
dc.relation.journalJournal of Medical Systemsen_US
dc.contributor.departmentİstanbul Arel Üniversitesi, Mühendislik ve Mimarlık Fakültesi, Bilgisayar Mühendisliği Bölümü.en_US
dc.contributor.authorIDTR40789en_US
dc.contributor.authorIDTR13219en_US
dc.contributor.authorIDTR29371en_US
dc.contributor.authorIDTR13946en_US
dc.contributor.authorIDTR2679en_US
dc.identifier.volume36en_US
dc.identifier.issue5en_US
dc.identifier.startpage2705en_US
dc.identifier.endpage2711en_US
dc.relation.publicationcategoryUluslararası Hakemli Dergien_US


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