A discriminative approach to automatic seizure detection in multichannel EEG signals

The aim of this paper is to introduce the application of Random Forests to the automated analysis of epileptic EEG data. Feature extraction is performed using a discrete wavelet transform to give time-frequency representations, from which statistical features based on the wavelet decompositions are formed and used for training and classification. We show that Random Forests can be used for the classification of ictal, inter-ictal and healthy EEG with a high level of accuracy, with 99% sensitivity and 93.5% specificity for classifying ictal and inter-ictal EEG, 90.6% sensitivity and 95.7% specificity for the windowed data and 93.9% sensitivity for seizure onset classification.