Samsung Announces Breakthrough Study in Fainting Prediction Using Galaxy Watch

Samsung Electronics Co., Ltd. today announced that a joint clinical study with Chung-Ang University Gwangmyeong Hospital in Korea has successfully validated the ability to predict vasovagal syncope (VVS) with high accuracy by utilizing biosignals obtained from Galaxy Watch6.¹

Vasovagal syncope occurs when a person’s heart rate and blood pressure drop abruptly due to factors like excessive stress, leading to a temporary loss of consciousness. While the fainting spell itself is typically not life-threatening, sudden falls can cause severe secondary injuries like fractures or concussions, making preemptive prediction critically important for prevention.

“It’s not uncommon for syncope patients to suffer trauma from falls, and in extreme cases, some experience severe injuries such as fractures or cerebral hemorrhage,” said Professor Junhwan Cho of the Department of Cardiology at Chung-Ang University Gwangmyeong Hospital. “An early warning from this technology could give patients advance time to get into a safe position or call for help, which would dramatically reduce the incidence of secondary injuries.”

The joint research team, led by Professor Cho, conducted evaluations on 132 patients with suspected VVS symptoms during induced fainting tests. Using the photoplethysmography (PPG) sensor on Galaxy Watch6 the team analyzed the watch’s heart rate variability (HRV) data with an AI algorithm. The model successfully predicted impending fainting episodes up to five minutes in advance with 84.6 percent accuracy.²

The research findings were published in Volume 7, Issue 4 of “European Heart Journal – Digital Health,” a leading medical journal, and they represent the world’s first study to successfully demonstrate the potential for a commercial smartwatch to provide early prediction of syncope.

Samsung plans to further advance the health monitoring capabilities of its wearable portfolio and expand collaboration with leading medical institutions. Through these efforts, the company aims to lead the digital health industry and accelerate the implementation of personalized, preventive health solutions.

1 Findings are based on the research study and do not reflect a feature currently available on the device.

2 Model performance was assessed using the AUROC and threshold metrics. 84.6 percent accuracy was achieved at a at a clinically meaningful sensitivity of 90 percent and a specificity of 64 percent.