Intelligent Signal Classifier for Brain Epileptic EEG Based on Decision Tree, Multilayer Perceptron and Over-Sampling Approach

  • Jimmy Ming-Tai Wu
  • Meng-Hsiun Tsai
  • Chia-Te Hsu
  • Hsien-Chung HuangEmail author
  • Hsiang-Chun Chen
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 70)


Epilepsy is a chronic neurological disease induced by abnormal electrical discharges of brain which tends to irregular seizures. The seizures may cause the patients to lose consciousness and the patients couldn’t control their muscles. Epilepsy even possibly endangers one’s life. Electroencephalogram (EEG) is a common tool used in the clinical diagnosis and analytics of epilepsy. However, the visual examination of EEG is time-consuming and the diagnostic result is also easily influenced by the viewer’s subjective judgement. Therefore, the purpose of this study is to construct an automatic classifier, which could be helpful to analyze, for the epileptic EEG signals. The EEG recordings of patients with intractable epilepsy, which are collected by Boston Children’s Hospital, are used in this study. The features of EEG signals in time and frequency domains are collected from results of the Fast Fourier Transform. The Synthetic Minority Oversampling Technique (SMOTE) is used to solve the data imbalance problem. Four machine learning algorithms including C4.5, Classification and Regression Tree (CART), Chi-Square Automatic Interaction Detector (CHAID) and Multilayer Perceptron (MLP) are used to classify the data. As a result, the accuracy rate of the proposed classifier is 99.48%. It might be a clinical assistant tool for doctors to make a more reliable and objective diagnosis.


Epilepsy Electroencephalogram Fast fourier transform Oversampling technique Machine learning algorithms 



The authors would like to thank the reviewers for their valuable suggestions and comments that are helpful to improve the content and quality for this paper. This paper is supported by the National Science Council of Taiwan, ROC, under the contract of MOST 106-3114-E-005-008–, MOST 106-2119-M-005-006– and the National Chung Hsing University-Chung Shan Medical University cooperative research project, under the contract of NCHU-CSMU 10707.


  1. 1.
    Awad, I.A., Rosenfeld, J., Ahl, J., Hahn, J.F., Lüders, H.: Intractable epilepsy and structural lesions of the brain: mapping, resection strategies, and seizure outcome. Epilepsia 32, 179–186 (1991)CrossRefGoogle Scholar
  2. 2.
    Fisher, R.S., Boas, W.V.E., Blume, W., Elger, C., Genton, P., Lee, P., et al.: Epileptic seizures and epilepsy: definitions proposed by the International League Against Epilepsy (ILAE) and the International Bureau for Epilepsy (IBE). Epilepsia 46, 470–472 (2005)CrossRefGoogle Scholar
  3. 3.
    Smeets, V.M., van Lierop, B.A., Vanhoutvin, J.P., Aldenkamp, A.P., Nijhuis, F.J.: Epilepsy and employment: literature review. Epilepsy Behav. 10, 354–362 (2007)CrossRefGoogle Scholar
  4. 4.
    Gotman, J., Gloor, P.: Automatic recognition and quantification of interictal epileptic activity in the human scalp EEG. Electroencephalogr. Clin. Neurophysiol. 41, 513–529 (1976)CrossRefGoogle Scholar
  5. 5.
    Yoo, J., Yan, L., El-Damak, D., Altaf, M.A.B., Shoeb, A.H., Chandrakasan, A.P.: An 8-channel scalable EEG acquisition SoC with patient-specific seizure classification and recording processor. IEEE J. Solid-State Circuits 48, 214–228 (2013)CrossRefGoogle Scholar
  6. 6.
    Shorvon, S.D.: Handbook of Epilepsy Treatment. Wiley, New York (2010)CrossRefGoogle Scholar
  7. 7.
    Rosenblatt, F.: The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 65, 386 (1958)CrossRefGoogle Scholar
  8. 8.
    Ray, W.J., Cole, H.W.: EEG alpha activity reflects attentional demands, and beta activity reflects emotional and cognitive processes. Science 228, 750–752 (1985)CrossRefGoogle Scholar
  9. 9.
    Chawla, N.V.: Data mining for imbalanced datasets: an overview. In: Data Mining and Knowledge Discovery Handbook, pp. 853–867. Springer, Berlin (2005)Google Scholar
  10. 10.
    Liu, X.-Y., Wu, J., Zhou, Z.-H.: Exploratory under-sampling for class-imbalance learning. 965–969 (2006)Google Scholar
  11. 11.
    Lewis, D.D., Catlett, J.: Heterogeneous uncertainty sampling for supervised learning. In: Proceedings of the Eleventh International Conference on Machine Learning, pp. 148–156 (1994)CrossRefGoogle Scholar
  12. 12.
    Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 321–357 (2002)CrossRefGoogle Scholar
  13. 13.
    Goldberger, A.L., Amaral, L.A., Glass, L., Hausdorff, L., Ivanov, P.C., Mark, R.G., et al.: Physiobank, physiotoolkit, and physionet components of a new research resource for complex physiologic signals. Circulation 101, 215–220 (2000)Google Scholar
  14. 14.
    Cooley, J.W., Tukey, J.W.: An algorithm for the machine calculation of complex Fourier series. Math. Comput. 19, 297–301 (1965)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques. Elsevier, Amsterdam (2011)zbMATHGoogle Scholar
  16. 16.
    Gardner, M.W., Dorling, S.: Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmos. Environ. 32, 2627–2636 (1998)CrossRefGoogle Scholar
  17. 17.
    Shoeb, A.H.: Application of Machine Learning to Epileptic Seizure Onset Detection and Treatment. Massachusetts Institute of Technology (2009)Google Scholar
  18. 18.
    Azami, H., Mohammadi, K., Bozorgtabar, B.: An improved signal segmentation using moving average and Savitzky-Golay filter (2012)CrossRefGoogle Scholar
  19. 19.
    Anusha, K., Mathews, M.T., Puthankattil, S.D.: Classification of normal and epileptic EEG signal using time & frequency domain features through artificial neural network. In: 2012 International Conference on Advances in Computing and Communications (ICACC), pp. 98–101 (2012)Google Scholar
  20. 20.
    Han, H., Wang, W.-Y., Mao, B.-H.: Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. In: International Conference on Intelligent Computing, pp. 878–887 (2005)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Jimmy Ming-Tai Wu
    • 1
  • Meng-Hsiun Tsai
    • 2
  • Chia-Te Hsu
    • 2
  • Hsien-Chung Huang
    • 2
    Email author
  • Hsiang-Chun Chen
    • 2
  1. 1.Shandong University of Science and TechnologyHuangdao District, QingdaoPeople’s Republic of China
  2. 2.National Chung Hsing UniversitySouth Dist, Taichung CityTaiwan (R.O.C.)

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