{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T03:43:45Z","timestamp":1790912625474,"version":"4.1.0"},"reference-count":33,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2026,4,5]],"date-time":"2026-04-05T00:00:00Z","timestamp":1775347200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,4,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Aims<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods and results<\/jats:title>\n                    <jats:p>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\u2005Hz). The HRV features (n = 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\u20131.00). At 90% sensitivity, specificity was 0.64 (95% CI 0.40\u20130.85). Using a fixed 5-min window, early prediction remained robust at a 5-min lead time (AUROC 0.91; 95% CI 0.76\u20131.00; accuracy 84.6%; 95% CI 0.65\u20130.92). 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).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Artificial intelligence-enabled analysis of smartwatch PPG\u2013derived 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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/ehjdh\/ztag053","type":"journal-article","created":{"date-parts":[[2026,3,29]],"date-time":"2026-03-29T11:34:32Z","timestamp":1774784072000},"source":"Crossref","is-referenced-by-count":2,"title":["Prediction of vasovagal syncope using artificial intelligence-enabled smartwatch photoplethysmography-derived heart rate variability"],"prefix":"10.1093","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2200-6601","authenticated-orcid":false,"given":"Hak Seung","family":"Lee","sequence":"first","affiliation":[{"name":"Medical AI Co., Ltd. , Seoul ,","place":["Republic of Korea"]},{"name":"Digital Healthcare Institute, Sejong Medical Research Institute , Bucheon ,","place":["Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junho","family":"Song","sequence":"additional","affiliation":[{"name":"Medical AI Co., Ltd. , Seoul ,","place":["Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Moonki","family":"Jung","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Heart and Brain Hospital, Chung-Ang University Gwangmyeong Hospital, Chung-Ang University College of Medicine , 110 Deokan-ro, Gwangmyeong-si, Gyeonggi-do 14353 ,","place":["Repulic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong-Yeon","family":"Jo","sequence":"additional","affiliation":[{"name":"Medical AI Co., Ltd. , Seoul ,","place":["Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joon-myoung","family":"Kwon","sequence":"additional","affiliation":[{"name":"Medical AI Co., Ltd. , Seoul ,","place":["Republic of Korea"]},{"name":"Digital Healthcare Institute, Sejong Medical Research Institute , Bucheon ,","place":["Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2158-8272","authenticated-orcid":false,"given":"Jun Hwan","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Heart and Brain Hospital, Chung-Ang University Gwangmyeong Hospital, Chung-Ang University College of Medicine , 110 Deokan-ro, Gwangmyeong-si, Gyeonggi-do 14353 ,","place":["Repulic of 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