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

• URL: http://arxiv.org/abs/2310.09236v1
• Date: Fri, 13 Oct 2023 16:40:29 GMT
• Title: Time CNN and Graph Convolution Network for Epileptic Spike Detection in MEG Data
• Authors: Pauline Mouches, Thibaut Dejean, Julien Jung, Romain Bouet, Carole Lartizien, Romain Quentin
• Abstract summary: 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. 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.
• Score: 1.9420255676093532
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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