ArticleEuropean heart journal. Digital health2026
Prediction of vasovagal syncope using artificial intelligence-enabled smartwatch photoplethysmography-derived heart rate variability.
Article in European heart journal. Digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Management of Syncope in Geriatric Patients: Are 2018 European Society of Cardiology Guidelines Still Relevant?Journal of clinical medicine · 2026Review
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Authors and funding
6 authors.
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Abstract
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: We prospectively enrolled 132 patients with suspected neurally mediated syncope who underwent head-up tilt (HUT) testing while wearing a wrist-worn Samsung Galaxy Watch 6 for continuous multiwavelength PPG acquisition (25 Hz). The HRV features ( 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.
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