TRACE: Episode-Local Transition Evidence for Zero-Shot Seizure Onset Localization in SEEG

In SEEG-based automated seizure analysis, accurate onset localization is important both for modeling seizure evolution and for supporting downstream clinical analysis. Existing methods face two key challenges: (1) detecting seizures reliably and localizing onset without priors such as patient identity, seizure type, or prior knowledge of seizure presence; and (2) generalizing across patients and centers despite substantial variation in background activity, seizure morphology, and acquisition conditions, without target-domain annotations, retraining, or parameter tuning. We propose TRACE, a framework based on episode-local transition evidence. TRACE encodes background-contrast features from each input clip into a shared channel–time evidence field that captures seizure-related changes relative to the episode-local background. A clip-level branch then summarizes that shared representation for seizure detection. When a clip is predicted as seizure-positive, the onset-localization branch selects high-evidence channels dynamically at each time point and constructs a global evidence trajectory by cross-channel pooling. A self-calibrated decoder reads that trajectory, identifies sustained seizure evidence, and backtracks to estimate the onset time. We evaluate our approach on SWEC-ETHZ and HUP under within-domain cross-patient settings. External zero-shot evaluation uses the patient-disjoint Pennsieve-530 cohort. On SWEC-ETHZ and HUP, our approach achieves oracle onset MAEs of 4.52 s and 4.92 s, reducing error by 37.7% and 34.0% relative to the strongest baseline in each cohort. On the spontaneous seizures of Pennsieve-530 (44 seizure and 132 non-seizure clips), our approach achieves a detection AUROC of 0.913 and an F1 score of 0.650, compared with 0.829 and 0.537 for the strongest baselines, while achieving an oracle onset MAE of 4.80 s, a 46.0% reduction relative to the strongest onset-localization baseline (8.89 s). These results show that constructing episode-local transition evidence and sparsely aggregating it provides an effective basis for cross-patient SEEG onset localization without target-domain adaptation.