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Updated: May 31, 2026

Electromagnetic Source Imaging in Presurgical Evaluation of Children with Drug-Resistant Epilepsy
Published on: September 20, 2024
Classification of juvenile myoclonic epilepsy data acquired through scanning electromyography with machine learning
Imran Goker1, Onur Osman, Serhat Ozekes
1Department of Management Information Systems, Okan University, Istanbul, Turkey. imran.goker@okan.edu.tr
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.
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