Combined Seizure Index with Adaptive Multi-Class SVM for epileptic EEG classification

In this paper, we have proposed a novel wavelet based CSI feature and a novel Adaptive Multi-Class Support Vector Machine (SVM) for the multi-class electroencephalogram (EEG) signals classification with the emphasis on epileptic seizure detection. The purpose was to determine an optimum classification scheme for this problem and also to infer clues about the extracted features. Wavelets have played an important role in biomedical signal processing for its ability to capture localized spatial-frequency information of EEG signal. CSI is used to develop a normalized index which state the maximum difference between the seizure and non-seizure states between the frequency range of 1-30Hz. The adaptive MSVM works well for high dimensional, multi-class data streams. Decision making was performed in two stages: feature extraction by computing the Combined Seizure Index and classification using the classifiers trained on the extracted features. We have compared the adaptive MSVM with the benchmark EEG dataset. Our experimental results show that the adaptive MSVM with wavelet based features which will represent the EEG signals and the classification methods trained on these features achieved high classification accuracies with better false rate and sensitivity.