EEG signals analysis using SVM and MLPNN classifiers for epilepsy detection

Electroencephalography (EEG) is an important tool for diagnosis of brain disorders such as epilepsy, it can measure the electrical activity of neurons and the recorded signal includes different characteristics in order to detect epileptic seizures. In this study, the analysis of the EEG signals was based on the Discrete Wavelet Transform (DWT) and some statistical features were extracted from the sub-bands to be as inputs in the Machine Learning (ML), by using two different classifiers: the Support Vector Machine (SVM) and Multilayer Perceptron Neural Network (MLPNN) for the automatic detection of this disease. Then, the performance of the classification process of both methods was presented and the results obtained by SVM and MLPNN are 99.5% and 100% of accuracy, respectively. Finally, our study shows that the two methods perform better in the detection of epilepsy and that the MLPNN achieved a higher accuracy.