Epileptic Seizure Classification of EEG Image Using SVM
International Journal of Innovative Research in Science Engineering and Technology
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
... 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. The DCT and DWT each extracts 40 features from the EEG image. The extracted features are applied to the SVM classifier, which classifies the image into two models 0 and 1. The EEG images are separated into training and testing groups each consisting of 100 images. The SVM learning is used to calculate the classification parameter. The images are classified into their respective models by the training process. Model 1 represents epilepsy and model 0 represents non epilepsy. Using the test images the accuracy of the SVM classifier is found. The results show that the SVM classification of EEG images using the features extracted from DWT has higher classification accuracy than the SVM classification that uses the features extracted by DCT.