SpaTeoGL: Spatiotemporal Graph Learning for Interpretable Seizure Onset Zone Analysis from Intracranial EEG

TL;DR

SpaTeoGL is a novel spatiotemporal graph learning framework that enhances the interpretability and accuracy of seizure onset zone localization from intracranial EEG data, aiding epilepsy surgery planning.

Contribution

It introduces a joint learning approach for spatial and temporal graphs within a smooth graph signal processing framework, improving seizure network analysis.

Findings

Outperforms baseline methods in SOZ identification.

Provides interpretable insights into seizure dynamics.

Achieves convergence guarantees with an efficient algorithm.

Abstract

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.