Journal of Medical Systems

, Volume 36, Issue 5, pp 2705–2711 | Cite as

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

  • Imran Goker
  • Onur OsmanEmail author
  • Serhat Ozekes
  • M. Baris Baslo
  • Mustafa Ertas
  • Yekta Ulgen
Original Paper


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.


Scanning electromyography Juvenile myoclonic epilepsy Feed-forward neural networks Support vector machines Decision trees Naïve bayes 


  1. 1.
    Stålberg, E., and Eriksson, P. O., A scanning electromyographic study of the topography of human masseter single motor units. Arch Oral Biol 32:793–797, 1987.CrossRefGoogle Scholar
  2. 2.
    Stålberg, E., and Falck, B., The role of EMG in neurology. Electroencephalogr Clin Neurophysiol 103:579–598, 1997.CrossRefGoogle Scholar
  3. 3.
    Preston, D. C, and B. E. Shapiro, Electromyography and neuromuscular disorders: clinical-electrophysiologic correlations, (Butterworth-Heinemann, Philadelphia, 2005).Google Scholar
  4. 4.
    Diószeghy, P., Scanning electromyography. Muscle Nerve Suppl 11:S66–S71, 2002.CrossRefGoogle Scholar
  5. 5.
    Gootzen, T. H. J. M., Vingerhoest, D. J. M., and Stegeman, D. F., A study of motor unit structure by means of scanning EMG. Muscle and Nerve 15:349–357, 1992.CrossRefGoogle Scholar
  6. 6.
    Goker, I., Baslo, B., Ertas, M., and Ulgen, Y., Large motor unit territories by scanning electromyography in patients with juvenile myoclonic epilepsy. Journal of Clinical Neurophysiology 27:212–215, 2010.CrossRefGoogle Scholar
  7. 7.
    Quinlan, J. R., C45: programs for machine learning. Morgan Kaufmann, San Mateo, 1993.Google Scholar
  8. 8.
    R. Lippmann, An Introduction to computing with neural nets, IEEE ASSP Magazine. 22, 1987.Google Scholar
  9. 9.
    V. N. Vapnik, The Nature of Statistical Learning Theory. Springer, 1998.Google Scholar
  10. 10.
    Su, Y., Shen, J., Qian, H., Ma, H., Ji, J., Ma, H., Ma, L., Zhang, W., Meng, L., Li, Z., Wu, J., Jin, G., Zhang, J., and Shou, C., Diagnosis of gastric cancer using decision tree classification of mass spectral data. Cancer Sci 98:37–43, 2007. doi: 10.1111/j.1349-7006.2006.00339.x.CrossRefGoogle Scholar
  11. 11.
    Markey, M. K., et al., Decision tree classification of proteins identified by mass spectrometry of blood serum samples from people with and without lung cancer. Proteomics 3:1678–1679, 2003.CrossRefGoogle Scholar
  12. 12.
    Silipo, R., Gori, M., Taddei, A., Varanini, M., and Marchesi, C., Classification of arrhythmic events in ambulatory electrocardiogram, using artificial neural networks. Comput Biomed Res 28:305–318, 1995.CrossRefGoogle Scholar
  13. 13.
    Maglogiannis, I. G., and Zafiropoulos, E. P., EP (2004) Characterization of digital medical images utilizing support vector machines. BMC Med Inform Decis Making 4:4, 2004.CrossRefGoogle Scholar
  14. 14.
    Nugent, T., and Jones, D. T., Transmembrane protein topology prediction using support vector machines. BMC Bioinformatics 10:159, 2009.CrossRefGoogle Scholar
  15. 15.
    Xue, Y., Chen, H., Jin, C., Sun, Z., and Yao, X., NBA-Palm: prediction of palmitoylation site implemented in Naïve Bayes algorithm. BMC Bioinformatics 7:458, 2006.CrossRefGoogle Scholar
  16. 16.
    Fan, L., Poh, K. L., and Zhou, P., A sequential feature extraction approach for Naïve bayes classification of microarray data. Expert Systems with Applications 36:9919–9923, 2009.CrossRefGoogle Scholar
  17. 17.
    Goker, I., Baslo, B., Ulgen, Y., and Ertas, M., Design of an experimental system for scanning electromyography method to investigate alterations of motor units in neurological disorders, Digest Journal of Nanomaterials and Biostructures 4:133–139, 2009.Google Scholar
  18. 18.
    Berson, A., Smith, S., and Thearling, K., Building Data Mining Applications for CRM. McGraw-Hill Professional Publishing, New York, 2000.Google Scholar
  19. 19.
    Han, J., and Kamber, M., Data mining concepts and techniques, the morgan kaufmann series in data management systems, 2nd edition. Elsevier Inc, San Francisco, 2006.Google Scholar
  20. 20.
    Agrawal, R., Imielinski, T., and Swami, A., Database mining: a performance perspective. IEEE Transactions on Knowledge and Data Engineering 5:914–925, 1993.CrossRefGoogle Scholar
  21. 21.
    B. E. Boser, I. Guyon, and V. Vapnik, A training algorithm for optimal margin classifiers, In Proceedings of the Fifth Annual Workshop on Computational Learning Theory, (1992), 144–152.Google Scholar
  22. 22.
    Cortes, C., and Vapnik, V., Support-vector network. Machine Learning 20:273–297, 1995.zbMATHGoogle Scholar
  23. 23.
    C.C. Chang, C. J. Lin, LIBSVM : A library for support vector machines, (2008).Google Scholar
  24. 24.
    M. Martínez, L. E. Sucar, Learning an optimal Naïve Bayes classifier, Proc. IEEE Inter. Conf. on Pattern Recognition (ICPR), China, (2006).Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Imran Goker
    • 1
  • Onur Osman
    • 2
    Email author
  • Serhat Ozekes
    • 3
  • M. Baris Baslo
    • 4
  • Mustafa Ertas
    • 5
  • Yekta Ulgen
    • 6
  1. 1.Faculty of Economics and Administrative Sciences, Department of Management Information SystemsOkan UniversityIstanbulTurkey
  2. 2.Faculty of Engineering and Architecture, Department of Electrical & Electronics EngineeringIstanbul Arel UniversityIstanbulTurkey
  3. 3.Faculty of Engineering and Architecture, Department of Computer EngineeringIstanbul Arel UniversityIstanbulTurkey
  4. 4.Istanbul University Capa Medical FacultyIstanbulTurkey
  5. 5.Anadolu Health CenterIstanbulTurkey
  6. 6.Institute of Biomedical EngineeringBogazici UniversityIstanbulTurkey

Personalised recommendations