Classification of seizure in EEG signals based on KPCA and SVM

Abstract

In this study, the electroencephalogram (EEG) signals-analysis experiments were made to classify seizures patients. Principal component analysis (PCA) and kernel principal component analysis (KPCA) were used for the data compression with the (EEG) signals. Classifiers based on support vector machine (SVM)-PCA and SVM-KPCA were designed. The classification performances of four kinds of kernel function were also compared using the same dataset. The results showed that using SVM-KPCA had higher recognition performance than SVM-PCA. Experimental results showed that the algorithm using SVM-KPCA with Gaussian-kernel had better recognition performance than the other three methods.