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

Goker I., Osman O., Ozekes S., BASLO M. B., Ertas M., Ulgen Y.

JOURNAL OF MEDICAL SYSTEMS, cilt.36, sa.5, ss.2705-2711, 2012 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 36 Sayı: 5
  • Basım Tarihi: 2012
  • Doi Numarası: 10.1007/s10916-011-9746-6
  • Dergi Adı: JOURNAL OF MEDICAL SYSTEMS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.2705-2711
  • Anahtar Kelimeler: Scanning electromyography, Juvenile myoclonic epilepsy, Feed-forward neural networks, Support vector machines, Decision trees, Naive bayes, DECISION TREE CLASSIFICATION, PREDICTION, NETWORKS, CANCER
  • Marmara Üniversitesi Adresli: Hayır

Özet

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 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.