Classifying discrete interval densities of EEG signals by using DWT and SVM

This study concentrates on detection of the epileptic activities in the electroencephalogram (EEG) signals. For this aim, features are extracted from the EEG signals by using first wavelet transform and then the approach of densities based on equal frequency discretization, and these features are classified by using support vector machines. The obtained results are compared with the results of three different studies. The results show that the feature extraction method used improves the classification success rate and SVM obtains the highest classification success rate possible in faster running time.