ST-GODE-UQ: A Spatiotemporal Graph Neural Ordinary Differential Equation Framework with Uncertainty Quantification for Sudden Cardiac Death Prediction

Sudden cardiac death (SCD) remains difficult to anticipate from routine risk stratification, and the reliability of electrocardiographic (ECG) predictions remains underreported. In this study, SCD prediction is investigated retrospectively using ECG segments sampled at predefined lead times before ventricular fibrillation (VF) onset. We propose ST-GODE-UQ, which represents 5-s ECG segments as frequency-band spectral-similarity graphs, performs continuous-depth latent learning with a graph neural ordinary differential equation (ODE), and combines heteroscedastic logit variance with Monte Carlo (MC) dropout. Evaluation used subject-wise grouped five-fold outer cross-validation in two complementary settings: a cross-database comparison of ECG segments recorded 30–35 min before VF with healthy normal-sinus-rhythm recordings, and a within-cohort temporal-control comparison of 30–35 and 80–85 min pre-VF segments from the same SCD subjects. ST-GODE-UQ achieved mean accuracies of 94.14% and 93.00% and mean ROC-AUCs of 94.78% and 93.60% in the cross-database and temporal-control evaluations, respectively. Its pooled out-of-fold ROC-AUC was numerically higher than that of the graph neural network (GNN) + Transformer baseline, although the paired difference was not statistically significant. Misclassified predictions showed higher median uncertainty across all three reliability measures, and controlled Gaussian-noise and baseline-wander perturbations progressively increased the aleatoric estimate. These findings support the feasibility of uncertainty-aware SCD prediction from predefined pre-VF ECG windows under subject-independent evaluation and motivate further validation in prospective and clinically heterogeneous settings.