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

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.

[1]  David T. Jones,et al.  Transmembrane protein topology prediction using support vector machines , 2009, BMC Bioinformatics.

[2]  E Stålberg,et al.  The role of electromyography in neurology. , 1997, Electroencephalography and clinical neurophysiology.

[3]  Ilias Maglogiannis,et al.  Characterization of digital medical images utilizing support vector machines , 2004, BMC Medical Informatics Decis. Mak..

[4]  Mustafa Ertas,et al.  Large Motor Unit Territories by Scanning Electromyography in Patients With Juvenile Myoclonic Epilepsy , 2010, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[5]  Peng Zhou,et al.  A sequential feature extraction approach for naïve bayes classification of microarray data , 2009, Expert Syst. Appl..

[6]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[7]  Tomasz Imielinski,et al.  Database Mining: A Performance Perspective , 1993, IEEE Trans. Knowl. Data Eng..

[8]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[9]  金田 重郎,et al.  C4.5: Programs for Machine Learning (書評) , 1995 .

[10]  Barbara E. Shapiro,et al.  Electromyography and neuromuscular disorders : clinical-electrophysiologic correlations , 1997 .

[11]  R. Lippmann,et al.  An introduction to computing with neural nets , 1987, IEEE ASSP Magazine.

[12]  I. GOKERa,et al.  DESIGN OF AN EXPERIMENTAL SYSTEM FOR SCANNING ELECTROMYOGRAPHY METHOD TO INVESTIGATE ALTERATIONS OF MOTOR UNITS IN NEUROLOGICAL DISORDERS , 2009 .

[13]  Alex Berson,et al.  Building Data Mining Applications for CRM , 1999 .

[14]  C. Floyd,et al.  Decision tree classification of proteins identified by mass spectrometry of blood serum samples from people with and without lung cancer , 2003, Proteomics.

[15]  Paul S. Giacomini,et al.  Electromyography and Neuromuscular Disorders: Clinical Electrophysiologic Correlations , 2006, McGill Journal of Medicine : MJM.

[16]  Peter Diószeghy.  Scanning electromyography , 2002, Muscle & nerve. Supplement.

[17]  Yu Xue,et al.  NBA-Palm: prediction of palmitoylation site implemented in Naïve Bayes algorithm , 2006, BMC Bioinformatics.

[18]  E Stålberg,et al.  A scanning electromyographic study of the topography of human masseter single motor units. , 1987, Archives of oral biology.

[19]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[20]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[21]  D. Stegeman,et al.  A study of motor unit structure by means of scanning EMG , 1992, Muscle & nerve.

[22]  Luis Enrique Sucar,et al.  Learning an Optimal Naive Bayes Classifier , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[23]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[24]  C Marchesi,et al.  Classification of arrhythmic events in ambulatory electrocardiogram, using artificial neural networks. , 1995, Computers and biomedical research, an international journal.

[25]  Y. Ulgen,et al.  Design of an experimental system for scanning EMG method to investigate alterations of motor units in neurological disorders , 2009, 2009 14th National Biomedical Engineering Meeting.

[26]  J. Ji,et al.  Diagnosis of gastric cancer using decision tree classification of mass spectral data , 2007, Cancer Science.

[27]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .