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

Bahareh Salafian, Eyal Fishel Ben-Knaan, Nir Shlezinger, Sandrine de Ribaupierre, Nariman Farsad

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.
Original languageEnglish
Title of host publicationICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Number of pages5
StatePublished - 2022


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