Intelligent Signal Classifier for Brain Epileptic EEG Based on Decision Tree, Multilayer Perceptron and Over-Sampling Approach
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.
KeywordsEpilepsy 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.
- 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.Liu, X.-Y., Wu, J., Zhou, Z.-H.: Exploratory under-sampling for class-imbalance learning. 965–969 (2006)Google Scholar
- 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
- 17.Shoeb, A.H.: Application of Machine Learning to Epileptic Seizure Onset Detection and Treatment. Massachusetts Institute of Technology (2009)Google Scholar
- 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.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