The Application of Approximate Entropy and Support Vector Machine in Classifying Signal of Epilepsy
Yang Zhang · Journal of Biomedical Engineering Research · 2013
To classify the EEG signals into interictal EEGs and ictal EEGs by the method of combination with ApEn and SVM,examine whether the nonlinear dynamic index can be effectively used in automatic detection of EEG epilepsy wave and the generalize ability of classifier trained by non-linear dynamics through the classification result.We used EEG from epileptic patient to train SVM and used it to classify EEG from other epileptic patients.It show that the SVM classifier practiced by nonlinear dynamic characteristics has a good generalizing ability;the classifier achieves a good classification result to different epileptic patients.