Abstrait

Epileptic Seizure Classification of EEG Image Using SVM

Pazhanirajan.S , Dhanalakshmi.P

In recent years humans suffer from various neurological disorders such as headache, dementia, traumatic brain injuries, strokes and epilepsy. Nearly 50 million people of the world population in all ages suffer from epilepsy. To diagnose epilepsy an automatic seizure detection system is an important tool. In this paper we present a new approach for classification of Electroencephalogram (EEG) signals into two categories namely epilepsy and non epilepsy. The features of the EEG images are extracted using Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT). The extracted features are used in the model generation. The pattern classification model of SVM observes the distribution of the EEG features of classes. The experiment on various EEG image illustrate that the results of SVM are significant and effective.