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

TL;DR

This paper introduces a combined Time CNN and GCN model for automated epileptic spike detection in MEG data, improving accuracy while considering sensor spatial relationships, thus aiding clinical diagnosis.

Contribution

It presents a novel hybrid model that integrates temporal CNN and graph convolutional networks to enhance spike detection in MEG data, with fewer parameters and better spatial awareness.

Findings

Achieved 76.7% F1-score on balanced dataset

Achieved 25.5% F1-score on imbalanced dataset

Outperformed existing deep learning methods

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.