Predicting seizure onset based on time-frequency analysis of EEG signals

Epilepsy is one of the chronic diseases of brain that occurs as a result of sudden and abnormal change in electric waves of brain. A lot of research has been carried out to predict the epileptic seizures. However, the literature shows that there are opportunities for further improvement in early seizure prediction. Therefore, in this paper, we aim to predict a seizure in advance with high prediction accuracy from EEG signals by using time-frequency feature extraction and classification techniques. An early prediction of an epileptic seizure will help clinicians and carers to properly intervene through medication or other preventive measures. Features from EEG signals were extracted using Discrete Wavelet Transformation (DWT) and then Support Vector Machine (SVM) and post-processing technique were applied to predict seizure in advance. We evaluated the performance of our proposed method in terms of sensitivity, specificity, and classification/prediction accuracy. The average prediction accuracy of our proposed method on ten patients was observed to be 96.38% and our method was able to predict a seizure 26.1 min before the actual seizure occurrence on average. The performance comparison of our proposed method with some existing techniques applied on similar datasets demonstrates that our proposed method can be an effective technique for the prediction of seizures. The findings project the towards this method’s possibility of future application in seizure prediction.