Classification of Juvenile Myoclonic Epilepsy Data Acquired Through Scanning Electromyography with Machine Learning Algorithms
JOURNAL OF MEDICAL SYSTEMS, cilt.36, sa.5, ss.2705-2711, 2012 (SCI-Expanded, Scopus)
- 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.