  We propose a convolutional neural network (CNN) aided factor graphs assisted
by mutual information features estimated by a neural network for seizure
detection. Specifically, we use neural mutual information estimation to
evaluate the correlation between different electroencephalogram (EEG) channels
as features. We then use a 1D-CNN to extract extra features from the EEG
signals and use both features to estimate the probability of a seizure
event.~Finally, learned factor graphs are employed to capture the temporal
correlation in the signal. Both sets of features from the neural mutual
estimation and the 1D-CNN are used to learn the factor nodes. We show that the
proposed method achieves state-of-the-art performance using 6-fold
leave-four-patients-out cross-validation.
