Prediction of vasovagal syncope using artificial intelligence-enabled smartwatch photoplethysmography-derived heart rate variability

Aims: Vasovagal syncope (VVS) can cause injury and impaired quality of life, and effective prevention requires timely warning before loss of consciousness. To evaluate whether smartwatch photoplethysmography (PPG)-derived heart rate variability (HRV) can predict VVS before symptom onset, and to identify an optimal observation window and lead time. Methods and results: = 107) were extracted. An Extra Trees classifier (600 trees) was trained using an 80/20 subject-level split and evaluated on a hold-out test set. Model performance was assessed using AUROC and threshold metrics, including specificity, at a fixed sensitivity of 0.90. Sixty-three participants were HUT-positive, and 69 were HUT-negative. The 5-min presyncope window achieved the highest discrimination (AUROC, 0.91; 95% CI 0.77-1.00). At 90% sensitivity, specificity was 0.64 (95% CI 0.40-0.85). Using a fixed 5-min window, early prediction remained robust at a 5-min lead time (AUROC 0.91; 95% CI 0.76-1.00; accuracy 84.6%; 95% CI 0.65-0.92). The most informative predictors included nonlinear complexity metrics (approximate entropy and composite multiscale entropy) and autonomic balance indices (normalized low-frequency, log-transformed high-frequency, and the cardiac vagal index). Conclusion: Artificial intelligence-enabled analysis of smartwatch PPG-derived HRV can prospectively predict VVS during HUT using a short 5-min observation window while maintaining clinically meaningful performance at a 5-min lead time, supporting the feasibility of wearable, real-time warning systems.