Goker, ImranOsman, OnurOzekes, SerhatBaslo, M. BarisErtas, MustafaUlgen, Yekta2024-05-252024-05-252012280148-55981573-689X10.1007/s10916-011-9746-62-s2.0-84867301569https://doi.org/10.1007/s10916-011-9746-6https://hdl.handle.net/20.500.14517/824Baslo, Mehmet Baris/0000-0003-1551-0559; osman, onur/0000-0001-7675-7999In 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.eninfo:eu-repo/semantics/closedAccessScanning electromyographyJuvenile myoclonic epilepsyFeed-forward neural networksSupport vector machinesDecision treesNaive bayesArticleQ1Q136527052711WOS:00030799440000121681512