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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.

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Juvenile myoclonic epilepsy is a genetically inherited disorder characterized by myoclonic jerks and generalized seizures. It has been proposed that patients with juvenile myoclonic epilepsy have larger motor units (MUs) than normals by MU number estimation and macro electromyography techniques. In this study, an experimental setup for scanning electromyography was built to investigate electrophysiologic cross-sections of the MU territories in 9 patients with juvenile myoclonic epilepsy, 3 patients with spinal muscular atrophy, and 10 healthy volunteers. Scanning electromyography was performed on the biceps brachii muscle. For each MU, three-dimensional maps of the MU territories were plotted. The length of MU cross-section and the maximum amplitude of each MU were measured from these maps and compared among the three groups of subjects. Like spinal muscular atrophy patients, patients with juvenile myoclonic epilepsy had significantly larger MU territories than normal controls.