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A Convolutional Gated Recurrent Neural Network for Seizure Onset Localization

The success of epileptic surgery highly depends on the accurate localization of the epileptic seizure. Seizure onset localization process is done using the intracranial Electroencephalogram (iEEG) recording which helps the physicians to determine the epileptogenic source in the brain. In this paper, we propose a supervised learning method based on a convolutional gated recurrent neural network to accurately analyze the non-stationary and nonlinear EEG signals. We study the effect of different hyperparameters like the number of convolutional layers and the number of kernels on the accuracy of such a difficult classification task. Discriminative spatio-temporal features are automatically extracted from the EEG signals by the convolutional neural network and the recurrent neural network. EEG feature extraction and classification applied to raw data are performed in a single automated system rather than extracting handcrafted features as in the previous work. High classification accuracy of 95.1% using ten-fold cross-validation testing strategy, makes the proposed method the most efficient among the state of the art.