Automated loss of pulse detection on a consumer smartwatch

Kamal Shah, Anran Wang, Yiwen Chen, Jitender Munjal, Sumeet Chhabra, Anthony Stange, Enxun Wei, Tuan Phan, Tracy Giest, Beszel Hawkins, Dinesh Puppala, Elsina Silver, Lawrence Cai, Shruti Rajagopalan, Edward Shi, Yun-Ling Lee, Matt Wimmer, Pramod Rudrapatna, Thomas Rea, Shelten Yuen, Anupam Pathak, Shwetak Patel, Mark Malhotra, Marc Stogaitis, Jeanie Phan, Bakul Patel, Adam Vasquez, Christina Fox, Alistair Connell, Jim Taylor, Jacqueline Shreibati, David Miller, Daniel McDuff, Pushmeet Kohli, Tajinder Gadh and Jake Sunshine ()
Additional contact information
Kamal Shah: Google Research
Anran Wang: Google Research
Yiwen Chen: Google Research
Jitender Munjal: US Heart & Vascular
Sumeet Chhabra: US Heart & Vascular
Anthony Stange: Google Research
Enxun Wei: Google Research
Tuan Phan: Google Research
Tracy Giest: Google Research
Beszel Hawkins: Google Research
Dinesh Puppala: Google Research
Elsina Silver: Google Research
Lawrence Cai: Google Research
Shruti Rajagopalan: Google Research
Edward Shi: Google Research
Yun-Ling Lee: Google Research
Matt Wimmer: Google Research
Pramod Rudrapatna: Google Research
Thomas Rea: King County Medic One, Emergency Medical Services Seattle, King County
Shelten Yuen: Google Research
Anupam Pathak: Google Research
Shwetak Patel: Google Research
Mark Malhotra: Google Research
Marc Stogaitis: Google Research
Jeanie Phan: Google Research
Bakul Patel: Google Research
Adam Vasquez: Google Research
Christina Fox: Google Research
Alistair Connell: Google Research
Jim Taylor: Google Research
Jacqueline Shreibati: Google Research
David Miller: DPM Biostatistics
Daniel McDuff: Google Research
Pushmeet Kohli: Google Deepmind
Tajinder Gadh: Google Research
Jake Sunshine: Google Research

Nature, 2025, vol. 642, issue 8066, 174-181

Abstract: Abstract Out-of-hospital cardiac arrest is a time-sensitive emergency that requires prompt identification and intervention: sudden, unwitnessed cardiac arrest is nearly unsurvivable1–3. A cardinal sign of cardiac arrest is sudden loss of pulse4. Automated biosensor detection of unwitnessed cardiac arrest, and dispatch of medical assistance, may improve survivability given the substantial prognostic role of time3,5, but only if the false-positive burden on public emergency medical systems is minimized5–7. Here we show that a multimodal, machine learning-based algorithm on a smartwatch can reach performance thresholds making it deployable at a societal scale. First, using photoplethysmography, we show that wearable photoplethysmography measurements of peripheral pulselessness (induced through an arterial occlusion model) manifest similarly to pulselessness caused by a common cardiac arrest arrhythmia, ventricular fibrillation. On the basis of the similarity of the photoplethysmography signal (from ventricular fibrillation or arterial occlusion), we developed and validated a loss of pulse detection algorithm using data from peripheral pulselessness and free-living conditions. Following its development, we evaluated the end-to-end algorithm prospectively: there was 1 unintentional emergency call per 21.67 user-years across two prospective studies; the sensitivity was 67.23% (95% confidence interval of 64.32% to 70.05%) in a prospective arterial occlusion cardiac arrest simulation model. These results indicate an opportunity, deployable at scale, for wearable-based detection of sudden loss of pulse while minimizing societal costs of excess false detections7.

Date: 2025
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DOI: 10.1038/s41586-025-08810-9

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