Classification of Juvenile Myoclonic Epilepsy Data Acquired Through Scanning Electromyography with Machine Learning Algorithms

Authors:
Imran Goker;Onur Osman;Serhat Ozekes;M. Baris Baslo;Mustafa Ertas;Yekta Ulgen
Affiliations:
Faculty of Economics and Administrative Sciences, Department of Management Information Systems, Okan University, Istanbul, Turkey;Faculty of Engineering and Architecture, Department of Electrical & Electronics Engineering, Istanbul Arel University, Istanbul, Turkey;Faculty of Engineering and Architecture, Department of Computer Engineering, Istanbul Arel University, Istanbul, Turkey;Istanbul University Capa Medical Faculty, Istanbul, Turkey;Anadolu Health Center, Istanbul, Turkey;Institute of Biomedical Engineering, Bogazici University, Istanbul, Turkey
Venue:
Journal of Medical Systems
Year:
2012

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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.