Validation of a Deep Learning Algorithm for Continuous, Real‐Time Detection of Atrial Fibrillation Using a Wrist‐Worn Device in an Ambulatory Environment
Ming‐Zher Poh, Anthony J. Battisti, Li‐Fang Cheng, Janice Lin, Anil Patwardhan, Ganesh S. Venkataraman, Charles A. Athill, Nimesh Patel, Chinmay Patel, Christian Machado, Jeffrey T. Ellis, Lori Crosson, Yuriko Tamura, R. Scooter Plowman, Mintu P. Turakhia, Hamid Ghanbari
Abstract
Background Wearable devices may be useful for identification, quantification and characterization, and management of atrial fibrillation (AF). To date, consumer wrist‐worn devices for AF detection using photoplethysmography‐based algorithms perform only periodic checks when the user is stationary and are US Food and Drug Administration cleared for prediagnostic uses without intended use for clinical decision‐making. There is an unmet need for medical‐grade diagnostic wrist‐worn devices that provide long‐term, continuous AF monitoring. Methods and Results We evaluated the performance of a wrist‐worn device with lead‐I ECG and continuous photoplethysmography (Verily Study Watch) and photoplethysmography‐based convolutional neural network for AF detection and burden estimation in a prospective multicenter study that enrolled 117 patients with paroxysmal AF. A 14‐day continuous ECG monitor (Zio XT) served as the reference device to evaluate algorithm sensitivity and specificity for detection of AF in 15‐minute intervals. A total of 91 857 intervals were contributed by 111 subjects with evaluable reference and test data (18.3 h/d median watch wear time). The watch was 96.1% sensitive (95% CI, 92.7%–98.0%) and 98.1% specific (95% CI, 97.2%–99.1%) for interval‐level AF detection. Photoplethysmography‐derived AF burden estimation was highly correlated with the reference device burden ( R 2 =0.986) with a mean difference of 0.8% (95% limits of agreement, −6.6% to 8.2%). Conclusions Continuous monitoring using a photoplethysmography‐based convolutional neural network incorporated in a wrist‐worn device has clinical‐grade performance for AF detection and burden estimation. These findings suggest that monitoring can be performed with wrist‐worn wearables for diagnosis and clinical management of AF. Registration Information URL: https://www.clinicaltrials.gov ; Unique identifier: NCT04546763.