Two-stage spatiotemporal graph neural network framework for seizure onset zone localization

Accurate localization of the seizure onset zone (SOZ) is critical for the presurgical evaluation of drug-resistant epilepsy. Intracranial electroencephalography captures high-resolution seizure dynamics, but interpreting complex spatiotemporal patterns across implanted electrodes is clinically challenging. We propose a two-stage, uncertainty-aware framework for SOZ localization that integrates patient-specific seizure modeling with cross-patient probabilistic ranking to estimate the likelihood of each electrode belonging to the SOZ.

The framework was evaluated on a bi-institutional cohort of 36 patients (20 with good surgical outcome, 16 with poor surgical outcome), comprising 184 seizures. In the first stage, a patient-specific spatiotemporal graph neural network modeled seizure onset as a network-level transition using directed functional connectivity graphs derived from burst-integrated electrophysiological features. Trained solely to detect preictal-to-ictal transitions, this model generated electrode probability trajectories without SOZ supervision. In the second stage, these trajectories were summarized into interpretable descriptors and used to train a patient-agnostic classifier assigning SOZ likelihood scores to electrodes. Evaluation and hyperparameter optimization were performed using nested leave-one-patient-out cross-validation restricted to good outcome patients, ensuring strict patient-level separation between training, validation, and test data.

Seizure onset detection was stable across patients, with comparable binary cross-entropy values between outcome groups (median = .495 vs. .438, p = .108). In good outcome patients, SOZ electrodes were prioritized among top-ranked contacts (area under the curve = .86 ± .06), with all patients exhibiting at least one SOZ electrode within the top positions. In poor outcome patients, predictions exhibited reduced spatial separability, lower likelihood magnitudes, and near-zero confidence gaps, reflecting attenuated model confidence.

This two-stage framework integrates spatial interactions, temporal dynamics, and probabilistic ranking within a pipeline. By providing graded SOZ likelihood estimates and uncertainty-aware prioritization, the approach is a decision-support tool to assist hypothesis refinement during presurgical evaluation rather than as a standalone diagnostic system.