Automated seizure detection from multichannel EEG signals using Support Vector Machine and Artificial Neural Networks

A method to automatically detect epileptic seizure regions from long term EEG recordings using Support Vector Machine (SVM) and Artificial Neural Network (ANN) classifier is proposed in this paper. This method uses a combination of various features derived from multichannel EEG signals. Features are extracted from a 4 second window to create a feature vector. Classifier (SVM/ANN) is trained using feature vectors from a carefully chosen training set. Feature vectors from a new data set when fed to the trained models will give an output which is then processed using different rules to remove interictal spikes and correctly detect the seizure regions. Results of applying this on long term EEG recordings of 27 epileptic patients revealed that, the proposed method is capable of very high degree of discrimination between the interictal region and ictal(seizure) region. The proposed method is a generalized seizure detection method which is not patient specific and has an average detection accuracy of nearly 75%.