Seizure detection in clinical EEG based on multi-feature integration and SVM

Abstract

Recurrence Quantification Analysis (RQA) was a nonlinear analysis method and widely used to analyze EEG signals. In this work, a feature extraction method based on the RQA measures was proposed to detect the epileptic EEG from EEG recordings. To combine the time-frequency characteristic of epileptic EEG, variation coefficient and fluctuation index were used to analyze epileptic EEG. The multi-feature combination of RQA and linear parameters had better performance in analyzing the nonlinear dynamic characteristics and time-frequency characteristic of epileptic EEG. For features selection and improving the classification accuracy, a support vector machine (SVM) classifier was used. The experimental results showed that the proposed method could classify the ictal EEG and interictal EEG with accuracy of 97.98%. © 2013 Springer-Verlag.