In this work, wavelet transform (WT) is used to analyze epileptic seizure in recorded EEG signals. Wavelets allow non-stationary EEG signals to be decomposed into elementary forms at different positions and scales. The extracted features from the WT decomposition is then expressed in terms wavelet based and classical based features to be further analyzed. In general, the coefficients of a 1-D wavelet decomposition comprises of approximate and detail coefficient, arranged in a single row. The number of wavelet coefficients depends on the decomposition level with more coefficients at high decomposition level. The features generated from wavelet transform is tested in terms of discriminatory information and the highly informative features will be identified. To select the best features, Fisher Discriminant Ratio (FDR) is implemented and classification error was calculated using Support Vector Machine (SVM). When FDR is applied, amongst all the 23 channels, certain channels will be dominant over the other channels in terms of value and these channels are then be chosen for the reduced feature analysis
