Time CNN and Graph Convolution Network for Epileptic Spike Detection in
  MEG Data
Pauline Mouches Thibaut Dejean Julien Jung Romain Bouet Carole Lartizien Romain Quentin
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.