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We propose a convolutional neural network (CNN) aided factor graphs assistedby mutual information features estimated by a neural network for seizuredetection. Specifically, we use neural mutual information estimation toevaluate the correlation between different electroencephalogram (EEG) channelsas features. We then use a 1D-CNN to extract extra features from the EEGsignals and use both features to estimate the probability of a seizureevent.~Finally, learned factor graphs are employed to capture the temporalcorrelation in the signal. Both sets of features from the neural mutualestimation and the 1D-CNN are used to learn the factor nodes. We show that theproposed method achieves state-of-the-art performance using 6-foldleave-four-patients-out cross-validation.

CNN-Aided Factor Graphs with Estimated Mutual Information Features for Seizure Detection