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CNN-AIDED FACTOR GRAPHS WITH ESTIMATED MUTUAL INFORMATION FEATURES FOR SEIZURE DETECTION

Citation Author(s):
Submitted by:
Bahareh Salafian
Last updated:
13 May 2022 - 4:35pm
Document Type:
Presentation Slides
Event:
Presenters:
Bahareh Salafian

Abstract

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.

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