Epileptic seizure detection from EEG signal using Discrete Wavelet Transform and Ant Colony classifier

Electroencephalogram (EEG) is the electrical signal of brain which contains valuable information about its activities. In this paper, we propose a new approach for the early detection of epileptic seizure in EEG. The proposed approach is based on Discrete Wavelet Transform (DWT) and Ant Colony (AC) Classifier. We started by applying DWT to decompose the EEG signal into its sub-bands to extract the energy ratio from wavelet coefficients. Beside we extract some statistical features from the original signal, and we use the extracted features as the input for the AC algorithm to derive classification rules, which are used to detect epileptic seizures in the EEG of the monitored patient. Our experimental results on real dataset show that our proposed approach achieves a high level of detection accuracy.