Time CNN and Graph Convolution Network for Epileptic Spike Detection in MEG Data

Pauline Mouches (1) , Thibaut Dejean (1) , Julien Jung (1) , Romain Bouet (1) , Carole Lartizien (2) , Romain Quentin (1)

1 CRNL - Centre de recherche en neurosciences de Lyon - Lyon Neuroscience Research Center
2 CREATIS - Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé

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.

Mots clés: MEG; Epilepsy; Classification; CNN; GCN

Domaines: Neurosciences [q-bio.NC]; Psychiatrie et santé mentale

Pauline Mouches, Thibaut Dejean, Julien Jung, Romain Bouet, Carole Lartizien, et al.. Time CNN and Graph Convolution Network for Epileptic Spike Detection in MEG Data. 2023. ⟨hal-04244255⟩