title: Classification of juvenile myoclonic epilepsy data acquired through scanning electromyography with machine learning algorithms.
abstract: In 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 Naïve 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.
authors.affiliation: Department of Management Information Systems, Okan University, Istanbul, Turkey. imran.goker@okan.edu.tr
authors.forename: Imran
authors.initials: I
authors.lastname: Goker
authors.forename: Onur
authors.initials: O
authors.lastname: Osman
authors.forename: Serhat
authors.initials: S
authors.lastname: Ozekes
authors.forename: M Baris
authors.initials: MB
authors.lastname: Baslo
authors.forename: Mustafa
authors.initials: M
authors.lastname: Ertas
authors.forename: Yekta
authors.initials: Y
authors.lastname: Ulgen
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003663:Decision Trees; D004576:Electromyography / Q000379:methods*; D006801:Humans; D020190:Myoclonic Epilepsy, Juvenile / Q000145:classification* / Q000175:diagnosis*; D016571:Neural Networks, Computer; D012372:ROC Curve; D015203:Reproducibility of Results; D060388:Support Vector Machine