Study of EEG based on SVM and SVM with EMD

Study electroencephalograph (EEG) of epileptic patients during different periods in order to do effective treatments. This paper studies wave forms and energy characteristics of the paroxysmal stage and the static epileptic EEG, adopts two different methods to classify. One is using Support Vector Machines (SVM) to rebuild and classify as to the characteristic signals. The other is applying Empirical Mode Decomposition (EMD) and SVM together to classify EEG. After experimenting and testing the epileptic data, the general discrimination of EEG is 94.5% and 96.05% respectively. The result indicates that using SVM and EMD can achieve more ideal effect, classify the epileptic EEG and normal EEG.