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

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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.

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References

  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.

    Article  Google Scholar 

  2. Stålberg, E., and Falck, B., The role of EMG in neurology. Electroencephalogr Clin Neurophysiol 103:579–598, 1997.

    Article  Google Scholar 

  3. Preston, D. C, and B. E. Shapiro, Electromyography and neuromuscular disorders: clinical-electrophysiologic correlations, (Butterworth-Heinemann, Philadelphia, 2005).

  4. Diószeghy, P., Scanning electromyography. Muscle Nerve Suppl 11:S66–S71, 2002.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  7. Quinlan, J. R., C45: programs for machine learning. Morgan Kaufmann, San Mateo, 1993.

    Google Scholar 

  8. R. Lippmann, An Introduction to computing with neural nets, IEEE ASSP Magazine. 22, 1987.

  9. V. N. Vapnik, The Nature of Statistical Learning Theory. Springer, 1998.

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  14. Nugent, T., and Jones, D. T., Transmembrane protein topology prediction using support vector machines. BMC Bioinformatics 10:159, 2009.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. Berson, A., Smith, S., and Thearling, K., Building Data Mining Applications for CRM. McGraw-Hill Professional Publishing, New York, 2000.

    Google Scholar 

  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. Agrawal, R., Imielinski, T., and Swami, A., Database mining: a performance perspective. IEEE Transactions on Knowledge and Data Engineering 5:914–925, 1993.

    Article  Google Scholar 

  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.

  22. Cortes, C., and Vapnik, V., Support-vector network. Machine Learning 20:273–297, 1995.

    MATH  Google Scholar 

  23. C.C. Chang, C. J. Lin, LIBSVM : A library for support vector machines, (2008).

  24. M. Martínez, L. E. Sucar, Learning an optimal Naïve Bayes classifier, Proc. IEEE Inter. Conf. on Pattern Recognition (ICPR), China, (2006).

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Authors and Affiliations

  1. Faculty of Economics and Administrative Sciences, Department of Management Information Systems, Okan University, Istanbul, Turkey

    Imran Goker

  2. Faculty of Engineering and Architecture, Department of Electrical & Electronics Engineering, Istanbul Arel University, Istanbul, Turkey

    Onur Osman

  3. Faculty of Engineering and Architecture, Department of Computer Engineering, Istanbul Arel University, Istanbul, Turkey

    Serhat Ozekes

  4. Istanbul University Capa Medical Faculty, Istanbul, Turkey

    M. Baris Baslo

  5. Anadolu Health Center, Istanbul, Turkey

    Mustafa Ertas

  6. Institute of Biomedical Engineering, Bogazici University, Istanbul, Turkey

    Yekta Ulgen

Authors
  1. Imran Goker
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  2. Onur Osman
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  3. Serhat Ozekes
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  4. M. Baris Baslo
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  5. Mustafa Ertas
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  6. Yekta Ulgen
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Correspondence to Onur Osman.

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Goker, I., Osman, O., Ozekes, S. et al. Classification of Juvenile Myoclonic Epilepsy Data Acquired Through Scanning Electromyography with Machine Learning Algorithms. J Med Syst 36, 2705–2711 (2012). https://doi.org/10.1007/s10916-011-9746-6

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  • DOI: https://doi.org/10.1007/s10916-011-9746-6

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