  We present and evaluate the capacity of a deep neural network to learn robust
features from EEG to automatically detect seizures. This is a challenging
problem because seizure manifestations on EEG are extremely variable both
inter- and intra-patient. By simultaneously capturing spectral, temporal and
spatial information our recurrent convolutional neural network learns a general
spatially invariant representation of a seizure. The proposed approach exceeds
significantly previous results obtained on cross-patient classifiers both in
terms of sensitivity and false positive rate. Furthermore, our model proves to
be robust to missing channel and variable electrode montage.
