Graph-Based Deep Learning on Stereo EEG for Predicting Seizure Freedom in Epilepsy Patients
Journal:
arXiv
Published Date:
Feb 21, 2025
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.
