Graph-Based Deep Learning on Stereo EEG for Predicting Seizure Freedom in Epilepsy Patients

TL;DR

This paper introduces a graph neural network model that uses stereo EEG data to predict seizure freedom in epilepsy patients, achieving high accuracy and identifying key brain regions involved.

Contribution

The study develops a novel GNN model integrating multi-scale attention to improve seizure outcome prediction from sEEG data, advancing personalized epilepsy treatment.

Findings

Achieved 92.4% accuracy in binary seizure freedom prediction

Identified anterior cingulate and frontal pole as key regions

Model's nodes coincided with seizure onset zones

Abstract

Predicting seizure freedom is essential for tailoring epilepsy treatment. But accurate prediction remains challenging with traditional methods, especially with diverse patient populations. This study developed a deep learning-based graph neural network (GNN) model to predict seizure freedom from stereo electroencephalography (sEEG) data in patients with refractory epilepsy. We utilized high-quality sEEG data from 15 pediatric patients to train a deep learning model that can accurately predict seizure freedom outcomes and advance understanding of brain connectivity at the seizure onset zone. Our model integrates local and global connectivity using graph convolutions with multi-scale attention mechanisms to capture connections between difficult-to-study regions such as the thalamus and motor regions. The model achieved an accuracy of 92.4% in binary class analysis, 86.6% in patient-wise analysis, and 81.4% in multi-class analysis. Node and edge-level feature analysis highlighted the anterior cingulate and frontal pole regions as key contributors to seizure freedom outcomes. The nodes identified by our model were also more likely to coincide with seizure onset zones. Our findings underscore the potential of new connectivity-based deep learning models such as GNNs for enhancing the prediction of seizure freedom, predicting seizure onset zones, connectivity analysis of the brain during seizure, as well as informing AI-assisted personalized epilepsy treatment planning.