Magnetoencephalography (MEG) recordings of patients with epilepsy exhibit spikes, a typical biomarker of the pathology. Detecting those spikes allows accurate localization of brain regions triggering seizures. Spike detection is often performed manually. However, it is a burdensome and error prone task due to the complexity of MEG data. To address this problem, we propose a 1D temporal convolutional neural network (Time CNN) coupled with a graph convolutional network (GCN) to classify short time frames of MEG recording as containing a spike or not. Compared to other recent approaches, our models have fewer parameters to train and we propose to use a GCN to account for MEG sensors spatial relationships. Our models produce clinically relevant results and outperform deep learning-based state-of-the-art methods reaching a classification f1-score of 76.7% on a balanced dataset and of 25.5% on a realistic, highly imbalanced dataset, for the spike class.
@article{arxiv.2310.09236,
title = {Time CNN and Graph Convolution Network for Epileptic Spike Detection in MEG Data},
author = {Pauline Mouches and Thibaut Dejean and Julien Jung and Romain Bouet and Carole Lartizien and Romain Quentin},
journal= {arXiv preprint arXiv:2310.09236},
year = {2023}
}
Comments
This work has been submitted to IEEE ISBI 2024 for possible publication