Passive Detection of Atrial Fibrillation Using a Commercially Available Smartwatch
Open accessAbstract
Importance: Atrial fibrillation (AF) affects 34 million people worldwide and is a leading cause of stroke. A readily accessible means to continuously monitor for AF could prevent large numbers of strokes and death. Objective: To develop and validate a deep neural network to detect AF using smartwatch data. Design, Setting, and Participants: In this multinational cardiovascular remote cohort study coordinated at the University of California, San Francisco, smartwatches were used to obtain heart rate and step count data for algorithm development. A total of 9750 participants enrolled in the Health eHeart Study and 51 patients undergoing cardioversion at the University of California, San Francisco, were enrolled between February 2016 and March 2017. A deep neural network was trained using a method called heuristic pretraining in which the network approximated representations of the R-R interval (ie, time between heartbeats) without manual labeling of training data. Validation was performed against the reference standard 12-lead electrocardiography (ECG) in a separate cohort of patients undergoing cardioversion. A second exploratory validation was performed using smartwatch data from ambulatory individuals against the reference standard of self-reported history of persistent AF. Data were analyzed from March 2017 to September 2017. Main Outcomes and Measures: The sensitivity, specificity, and receiver operating characteristic C statistic for the algorithm to detect AF were generated based on the reference standard of 12-lead ECG-diagnosed AF. Results: Of the 9750 participants enrolled in the remote cohort, including 347 participants with AF, 6143 (63.0%) were male, and the mean (SD) age was 42 (12) years. There were more than 139 million heart rate measurements on which the deep neural network was trained. The deep neural network exhibited a C statistic of 0.97 (95% CI, 0.94-1.00; P < .001) to detect AF against the reference standard 12-lead ECG-diagnosed AF in the external validation cohort of 51 patients undergoing cardioversion; sensitivity was 98.0% and specificity was 90.2%. In an exploratory analysis relying on self-report of persistent AF in ambulatory participants, the C statistic was 0.72 (95% CI, 0.64-0.78); sensitivity was 67.7% and specificity was 67.6%. Conclusions and Relevance: This proof-of-concept study found that smartwatch photoplethysmography coupled with a deep neural network can passively detect AF but with some loss of sensitivity and specificity against a criterion-standard ECG. Further studies will help identify the optimal role for smartwatch-guided rhythm assessment.
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Atrial fibrillation screening through PPG has shown variable accuracy depending on the algorithms and devices used.
“AF screening through PPG has shown variable accuracy depending on the algorithms and devices used19,51,52”
From Smart wearable devices in cardiovascular care: Where we are and how to move forward (2021) · cites this paper as [51]
A deep neural network using Apple Watch-based PPG sensor data showed excellent prediction of AF (C-statistic 0.97) against the gold standard 12-lead ECG.
“In a proof-of-concept study involving the use of Apple Watch-based PPG sensor data, a deep neural network (DNN) algorithm trained with heuristic pretraining showed excellent prediction of AF (C-statistic 0.97) against the gold standard 12-lead ECG51.”
From Smart wearable devices in cardiovascular care: Where we are and how to move forward (2021) · cites this paper as [51]
There is a clear need for ambulatory biosignals, since the performance of an algorithm developed from resting-setting data is quite poor in an ambulatory setting.
“However, there is a clear need for ambulatory biosignals, since the performance of an algorithm developed from the data of a resting setting is quite poor in an ambulatory setting.64”
From Artificial intelligence for detection of cardiovascular-related diseases from wearable devices: A systematic review and meta-analysis (2022) · cites this paper as [64]
One study reported difficulty for an algorithm trained on resting ECG data to show proper prediction performance when applied to ambulatory ECG data.
“One study, in particular, reported difficulty for an algorithm trained on resting ECG data to show proper prediction performance when applied to ambulatory ECG data.64”
From Artificial intelligence for detection of cardiovascular-related diseases from wearable devices: A systematic review and meta-analysis (2022) · cites this paper as [64]
A model that is developed using resting biosignals as a source rarely works well for ambulatory biosignals.
“As mentioned earlier, a model that is developed using resting biosignals as a source rarely works well for ambulatory biosignals.64”
From Artificial intelligence for detection of cardiovascular-related diseases from wearable devices: A systematic review and meta-analysis (2022) · cites this paper as [64]
The potential utility of wrist-worn PPG devices for detecting possible AF has been assessed in recent studies.
“The potential utility of wrist-worn PPG devices for detecting possible AF has been assessed in recent studies”
From Wearable Photoplethysmography for Cardiovascular Monitoring (2022) · cites this paper as [197]
Researchers are increasingly using consumer wearables or wrist-worn smartwatches and fitness monitors for digital measurement of cardiovascular health and psychophysiological stress because these devices allow continuous, scalable, unobtrusive big data collection of cardiac activity in real-world conditions with large samples.
“Researchers are increasingly using consumer wearables or wrist-worn smartwatches and fitness monitors for the digital measurement of cardiovascular health and psychophysiological stress as these devices allow for continuous, scalable, and unobtrusive, “big data” collection of overall cardiac activity in real-world conditions with large samples25–30.”
From Guidelines for wrist-worn consumer wearable assessment of heart rate in biobehavioral research (2020) · cites this paper as [29]
Tison's group used a PPG sensor and accelerometer of a commercial smartwatch to obtain HR data and steps; their device can detect atrial fibrillation with sensitivity 98% and specificity 90.2%.
“Thanks to the algorithm based on heuristic pretraining and deep neural network training, their device can detect the fibrillation with sensitivity of 98% and specificity of 90.2% [83].”
From The Current State of Optical Sensors in Medical Wearables (2022) · cites this paper as [83]
The DNN algorithm trained based off of the AW PPG sensor data has shown excellent prediction of AF compared with the gold standard 12-lead ECGs (c-statistic, 0.97; 84% positive predictive value).
“The DNN algorithm trained base off of the AW PPG sensor data has shown excellent prediction of AF compared with the gold standard 12-lead ECGs (c-statistic, 0.97; 84% positive predictive value).24”
From Unlocking Tomorrow’s Health Care: Expanding the Clinical Scope of Wearables by Applying Artificial Intelligence (2024) · cites this paper as [24]
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Jin, D., Adams, H., Cocco, A. M., Martin, W. G., & Palmer, S. (2020) · Medical Journal of Australia · doi:10.5694/mja2.50446 · Cited by 20 · see citing works
Continuous heart rhythm monitoring using mobile photoplethysmography in ambulatory patients
Hochstadt, A., Havakuk, O., Chorin, E., Schwartz, A. L., Merdler, I., Laufer, M., Lubman, N., Ghantous, E., Viskin, S., & Rosso, R. (2020) · Journal of Electrocardiology · doi:10.1016/j.jelectrocard.2020.04.017 · Cited by 18 · see citing works
Challenges and Potential of Using Digital Biomarkers in Healthcare and Clinical Trials
Lieberwirth, J. K., Mittermaier, M., & Stern, A. D. (2026) · Communications Medicine · doi:10.1038/s43856-026-01450-8 · Cited by 13 · see citing works
- Authors
- Tison, G. H., Sanchez, J. M., Ballinger, B., Singh, A., Olgin, J. E., Pletcher, M. J., Vittinghoff, E., Lee, E. S., Fan, S. M., Gladstone, R. A., Mikell, C., Sohoni, N., Hsieh, J., & Marcus, G. M.
- Affiliations
- University of California, San Francisco (US)
- Division of Cardiology, Department of Medicine, University of California, San Francisco
- Cardiogram Incorporated, San Francisco, California
- Corresponding author
- Marcus, Gregory M.
- ORCID
- Venue
- JAMA Cardiology
- Publisher
- American Medical Association (AMA)
- Published
- 2018-05-01
- Volume
- 3
- Issue
- 5
- Pages
- 409
- ISSN
- 2380-6583 (ISSN-L)
- PMC
- PMC5875390
- OpenAlex
- W2790444357
- Cited by
- 566
- References
- 35
- Citations by year
- 2026: 60 · 2025: 65 · 2024: 52 · 2023: 80 · 2022: 73 · 2021: 75 · 2020: 75 · 2019: 63 · 2018: 22
- FWCI
- 44.38
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- JAMA Cardiology (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Atrial Fibrillation Management and Outcomes
- Keywords
- atrial fibrillation, passive detection, smartwatches, wearable devices, deep neural network, heart rate, RR interval, cardioversion, 12-lead ECG, stroke prevention, remote monitoring, step count, algorithm validation, sensitivity and specificity, concordance index
- MeSH terms
- Adult, Algorithms, Atrial Fibrillation, Electric Countershock, Female, Humans, Male, Middle Aged, Reproducibility of Results, Photoplethysmography, Monitoring, Ambulatory, Mobile Applications
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