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

TL;DR

This paper introduces a novel seizure detection method combining CNN-extracted features with neural mutual information estimates within factor graphs, achieving state-of-the-art results on EEG data.

Contribution

It presents a new approach integrating neural mutual information estimation with CNN features in factor graphs for improved seizure detection.

Findings

Achieves state-of-the-art seizure detection performance.

Effectively captures temporal correlations in EEG signals.

Combines mutual information and CNN features for enhanced accuracy.

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.