Seizure detection in EEG using time frequency analysis and SVM

Abstract

Many brain disorders are diagnosed by analysing the EEG signals. EEG refers to the recording of the brain's spontaneous electrical activity over a short period of time. In this paper an efficient approach for detecting the presence of epileptic seizures in EEG signals is presented. Epilepsy is a disease due to temporary alternation in brain functions due to abnormal electrical activity of a group of brain cells and is termed as seizure. The analysis is performed in three stages. In the first step the Discrete wavelet transform is used for decompose the EEG signal into delta, theta, alpha, beta and gamma subbands. In the second step the statistical features are extracted from each subband and finally classification of the EEG signal that is epileptic seizure exists or not has been done using support vector machine. This method is applied for two different groups of EEG signals: 1) healthy (Normal) EEG dataset; 2) epileptic dataset during a seizure interval. The experimental results show that the proposed method efficiently detects the presence of epileptic seizure in EEG signals and also showed a reasonable accuracy in detection.