Detection of Atrial Fibrillation Using a Ring-Type Wearable Device (CardioTracker) and Deep Learning Analysis of Photoplethysmography Signals: Prospective Observational Proof-of-Concept Study

Soonil Kwon,Seil Oh,Eue-Keun Choi,Bon-Kwon Koo,Changhyun Baik,Yung Yi,Euijae Lee,Joonki Hong,Byunghwan Lee,Eui-Rim Jeong

doi:10.2196/16443

Abstract

BackgroundContinuous photoplethysmography (PPG) monitoring with a wearable device may aid the early detection of atrial fibrillation (AF).ObjectiveWe aimed to evaluate the diagnostic performance of a ring-type wearable device (CardioTracker, CART), which can detect AF using deep learning analysis of PPG signals.MethodsPatients with persistent AF who underwent cardioversion were recruited prospectively. We recorded PPG signals at the finger with CART and a conventional pulse oximeter before and after cardioversion over a period of 15 min (each instrument). Cardiologists validated the PPG rhythms with simultaneous single-lead electrocardiography. The PPG data were transmitted to a smartphone wirelessly and analyzed with a deep learning algorithm. We also validated the deep learning algorithm in 20 healthy subjects with sinus rhythm (SR).ResultsIn 100 study participants, CART generated a total of 13,038 30-s PPG samples (5850 for SR and 7188 for AF). Using the deep learning algorithm, the diagnostic accuracy, sensitivity, specificity, positive-predictive value, and negative-predictive value were 96.9%, 99.0%, 94.3%, 95.6%, and 98.7%, respectively. Although the diagnostic accuracy decreased with shorter sample lengths, the accuracy was maintained at 94.7% with 10-s measurements. For SR, the specificity decreased with higher variability of peak-to-peak intervals. However, for AF, CART maintained consistent sensitivity regardless of variability. Pulse rates had a lower impact on sensitivity than on specificity. The performance of CART was comparable to that of the conventional device when using a proper threshold. External validation showed that 94.99% (16,529/17,400) of the PPG samples from the control group were correctly identified with SR.ConclusionsA ring-type wearable device with deep learning analysis of PPG signals could accurately diagnose AF without relying on electrocardiography. With this device, continuous monitoring for AF may be promising in high-risk populations.Trial RegistrationClinicalTrials.gov NCT04023188; https://clinicaltrials.gov/ct2/show/NCT04023188

Highlights

MethodsAtrial fibrillation (AF) is the most common cardiac arrhythmia, and its prevalence has rapidly increased, especially in the elderly population [1]
We aimed to evaluate the diagnostic performance of a ring-type wearable device (CardioTracker, CART), which can detect atrial fibrillation (AF) using deep learning analysis of PPG signals
This study aimed to develop a ring-type wearable device (CardioTracker, CART) to detect AF with deep learning analysis of PPG signals and to evaluate its diagnostic performance in patients with AF

Summary

Introduction

MethodsAtrial fibrillation (AF) is the most common cardiac arrhythmia, and its prevalence has rapidly increased, especially in the elderly population [1]. The diagnosis of early AF tends to be delayed because the condition is often asymptomatic [6] This unmet need for early AF detection may be relieved by continuously monitoring the cardiac rhythm in high-risk populations [7]. Continuous photoplethysmography (PPG) monitoring with a wearable device may aid the early detection of atrial fibrillation (AF). Objective: We aimed to evaluate the diagnostic performance of a ring-type wearable device (CardioTracker, CART), which can detect AF using deep learning analysis of PPG signals. Conclusions: A ring-type wearable device with deep learning analysis of PPG signals could accurately diagnose AF without relying on electrocardiography. With this device, continuous monitoring for AF may be promising in high-risk populations.

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