Hybrid Feature Extraction Technique for Multi-Classification of Ictal and Non-Ictal EEG Epilepsy Signals


  • Sani Saminu Hebei University of Technology Tianjin
  • Guizhi Xu Hebei University of Technology Tianjin
  • Shuai Zhang Hebei University of Technology Tianjin
  • Abd El Kader Isselmou Hebei University of Technology Tianjin
  • Adamu Halilu Jabire Hebei University of Technology Tianjin
  • Ibrahim Abdullahi Karaye Hebei University of Technology Tianjin
  • Isah Salim Ahmad Hebei University of Technology Tianjin




These Electroencephalography (EEG) signals is an effective tool for identification, monitoring, and treatment of epilepsy, but EEG signals need highly experienced personnel to interpret it correctly due to its complexity, even for an expert it is monotonous and usually consume much time. Therefore, the automatic computer-aided device (CAD) needs to be developed to overcome those challenges associated with epilepsy interpretation and diagnosis. The system efficiency relies largely on the quality of features supply as input to classifiers. This paper presents an efficient feature extraction technique to develop a CAD system that can detect and classify normal, interictal and ictal epilepsy signals correctly with high accuracy. Our approach employs time-frequency features, statistical features and nonlinear features combined as hybrid features to train and test the classifier. Machine learning classifiers of multi-class support vector machine (mSVM) and feed-forward neural network (FFNN) with fivefold cross-validation are used to classifies normal, interictal and ictal with our proposed features. Our system was tested using a publicly available database with three classes each of 100 single channels EEG signals of 4096 samples point each. Based on sensitivity, specificity, and accuracy, our proposed approach of multiclass classification shows a good performance with 96.7%, 98.3% and 100% of sensitivity, specificity, and accuracy respectively.


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How to Cite

Saminu, S., Xu, G., Zhang, S., Isselmou, A. E. K., Jabire, A. H., Karaye, I. A., & Ahmad, I. S. (2020). Hybrid Feature Extraction Technique for Multi-Classification of Ictal and Non-Ictal EEG Epilepsy Signals. ELEKTRIKA- Journal of Electrical Engineering, 19(2), 1–11. https://doi.org/10.11113/elektrika.v19n2.219