Time CNN and Graph Convolution Network for Epileptic Spike Detection in MEG Data - EDUWELL: Experiential Neuroscience and Mental Training
Pré-Publication, Document De Travail (Preprint/Prepublication) Année : 2023

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

Résumé

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.
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Dates et versions

hal-04244255 , version 1 (29-11-2023)

Identifiants

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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⟩
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