An Approach for Predicting Clinical EEG Signal Based on SVM

Abstract

Accurate modeling of Electroencephalography (EEG) signals is an important problem in clinical diagnosis of brain diseases. The method using support vectors machine (SVM) based on the structure risk minimization provides us an effective way of learning machine. But solving the quadratic programming problem for training SVM becomes a bottle-neck of using SVM because of the long time of SVM training. In this paper, a local-SVM method is proposed for modeling EEG signals. The local method is presented for improving the speed of the prediction of EEG signals. Furthermore, this proposed model is used to detect epilepsy from EEG signals in which dynamical characteristics are difference between normal and epilepsy EEG signals. The experimental results show that the training of the local-SVM obtains a good behavior. In addition, the local SVM method significantly improves the prediction and detection precision.