Passive Detection of Atrial Fibrillation Using a Commercially Available Smartwatch

Abstract

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.

Conflict of interest statement

Conflicts of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure and Potential Conflicts of Interest. Dr Tison is an advisor to Cardiogram Inc. Messrs Ballinger, Singh, Sohoni, and Hsieh are employees of Cardiogram Inc. Dr Marcus has received research funding from Medtronic and Cardiogram Inc, is a consultant for Lifewatch and InCarda, and holds equity in InCarda. No other disclosures were reported.

Figures

Figure 1.. T-Distributed Stochastic Neighbor Embedding Visualization of the Deep Neural Network’s Last Layer Using Data From the Cardioversion Cohort

This t-distributed stochastic neighbor embedding, which is a technique that assists in visualizing high-dimensional data in 2 dimensions, depicts the deep neural network’s internal representation of the data derived from the last recurrent layer of the neural network. Each point represents a 10-minute segment of data from our validation (cardioversion) data set; orange points represent atrial fibrillation segments (precardioversion) and blue points represent normal sinus rhythm segments (postcardioversion). The neural network has largely clustered atrial fibrillation from normal sinus rhythm segments, as depicted when plotted on 2 dimensions (axes) that were chosen arbitrarily. Most points classified as normal sinus rhythm are in the upper part of the visualization, while atrial fibrillation points are separated in alternate clusters. The upper inset shows an example of raw smartwatch heart rate data associated with normal sinus rhythm, and the lower inset shows raw atrial fibrillation smartwatch data; each vertical bar represents a 5-second average heart rate color-coded by beats per minute (BPM; blue,

Figure 2.. Accuracy of Detecting Atrial Fibrillation in the Cardioversion Cohort

A, Receiver operating characteristic curve among 51 individuals undergoing in-hospital cardioversion. The curve demonstrates a C statistic of 0.97 (95% CI, 0.94-1.00), and the point on the curve indicates a sensitivity of 98.0% and a specificity of 90.2%. B, Receiver operating characteristic curve among 1617 individuals in the ambulatory subset of the remote cohort. The curve demonstrates a C statistic of 0.72 (95% CI, 0.64-0.78), and the point on the curve indicates a sensitivity of 67.7% and a specificity of 67.6%.