SpaTeoGL: Spatiotemporal Graph Learning for Interpretable Seizure Onset Zone Analysis from Intracranial EEG
This work addresses a critical challenge in epilepsy surgery by offering an interpretable method for analyzing seizure dynamics, though it appears incremental as it builds on existing graph-based approaches.
The paper tackled the problem of localizing the seizure onset zone from intracranial EEG for epilepsy surgery by proposing SpaTeoGL, a spatiotemporal graph learning framework, which showed competitive performance with a baseline while improving non-SOZ identification and providing interpretable insights.
Accurate localization of the seizure onset zone (SOZ) from intracranial EEG (iEEG) is essential for epilepsy surgery but is challenged by complex spatiotemporal seizure dynamics. We propose SpaTeoGL, a spatiotemporal graph learning framework for interpretable seizure network analysis. SpaTeoGL jointly learns window-level spatial graphs capturing interactions among iEEG electrodes and a temporal graph linking time windows based on similarity of their spatial structure. The method is formulated within a smooth graph signal processing framework and solved via an alternating block coordinate descent algorithm with convergence guarantees. Experiments on a multicenter iEEG dataset with successful surgical outcomes show that SpaTeoGL is competitive with a baseline based on horizontal visibility graphs and logistic regression, while improving non-SOZ identification and providing interpretable insights into seizure onset and propagation dynamics.