High Specificity Wearable Device With Photoplethysmography and Six-Lead Electrocardiography for Atrial Fibrillation Detection Challenged by Frequent Premature Contractions: DoubleCheck-AF

Abstract

BackgroundConsumer smartwatches have gained attention as mobile health (mHealth) tools able to detect atrial fibrillation (AF) using photoplethysmography (PPG) or a short strip of electrocardiogram (ECG). PPG has limited accuracy due to the movement artifacts, whereas ECG cannot be used continuously, is usually displayed as a single-lead signal and is limited in asymptomatic cases.ObjectiveDoubleCheck-AF is a validation study of a wrist-worn device dedicated to providing both continuous PPG-based rhythm monitoring and instant 6-lead ECG with no wires. We evaluated its ability to differentiate between AF and sinus rhythm (SR) with particular emphasis on the challenge of frequent premature beats.Methods and ResultsWe performed a prospective, non-randomized study of 344 participants including 121 patients in AF. To challenge the specificity of the device two control groups were selected: 95 patients in stable SR and 128 patients in SR with frequent premature ventricular or atrial contractions (PVCs/PACs). All ECG tracings were labeled by two independent diagnosis-blinded cardiologists as “AF,” “SR” or “Cannot be concluded.” In case of disagreement, a third cardiologist was consulted. A simultaneously recorded ECG of Holter monitor served as a reference. It revealed a high burden of ectopy in the corresponding control group: 6.2 PVCs/PACs per minute, bigeminy/trigeminy episodes in 24.2% (31/128) and runs of ≥3 beats in 9.4% (12/128) of patients. AF detection with PPG-based algorithm, ECG of the wearable and combination of both yielded sensitivity and specificity of 94.2 and 96.9%; 99.2 and 99.1%; 94.2 and 99.6%, respectively. All seven false-positive PPG-based cases were from the frequent PVCs/PACs group compared to none from the stable SR group (P < 0.001). In the majority of these cases (6/7) cardiologists were able to correct the diagnosis to SR with the help of the ECG of the device (P = 0.012).ConclusionsThis is the first wearable combining PPG-based AF detection algorithm for screening of AF together with an instant 6-lead ECG with no wires for manual rhythm confirmation. The system maintained high specificity despite a remarkable amount of frequent single or multiple premature contractions.

Accuracy of Artificial Intelligence-Based Technologies for the Diagnosis of Atrial Fibrillation: A Systematic Review and Meta-Analysis

“…The smallest study included 51 patients [30], and the biggest, 1057 [31]. Five studies were conducted in an outpatient setting [11,[31][32][33][34], five studies in an inpatient setting [30,[35][36][37][38] and two studies in both settings [39,40]. Eight studies [30,31,33,[36][37][38][39][40] used smartwatches, three studies [11,32,35] used mobile phones and one study [34] tested the technology of a remote PPG with the use of industrial camera.…”

Diagnostic validation of smart wearable device embedded with single-lead electrocardiogram for arrhythmia detection

“…The sensitivities, specificities, and accuracies of the algorithms and methods used to classify AF, sinus rhythm and ectopic beats were all over 94%. 25 , 26 Our algorithm, which is based on a single-lead ECG showed an ability to distinguish between sinus rhythm, ectopic beats, and AF similar to that of a 12-lead ECG. Moreover, the macro-F1 score of our algorithm based on single-lead ECG for the four-class classification system of sinus rhythm, AF, atrial premature beats, and ventricular premature beats was 92.9%, which was also comparable to the results of previous studies those were based on two-channel ambulatory ECGs or a 6-lead ECG.…”

“…Most of these studies were based on two-channel ambulatory ECGs or a 6-lead ECG. Three studies (Casas et al, 22 Zhipeng Cai et al, 23 , and Yang et al 24 ) focused on the classification of normal sinus rhythm and ectopic beats, while the other two studies (Bacevicius et al 25 and Zhang et al 26 ) attempted to differentiate AF from sinus rhythm and ectopic beats. The F1 scores of the algorithms and methods used to classify sinus rhythm and ectopic beats were all over 90.0%, except for the F1 score in Yang's study, 24 where it was 72.2% for classifying premature atrial complex.…”

Deep-Learning-Based Estimation of the Spatial QRS-T Angle from Reduced-Lead ECGs

“…Our model measured the spatial QRS-T angle with reasonable accuracy from a set of three frontal-and-one precordial leads ({ I , II , aVF , V2 }) that can be registered with three electrodes instead of the eight required to derive the QRS-T angle using the conventional approach. Requiring one precordial lead evidently restricts the type of consumer healthcare devices suitable for deploying our deep learning model in future applications, precluding the use of devices that maximize comfort, such as wrist-worn wearables [ 47 ], which only register frontal-lead ECGs. Nevertheless, the market already offers a handful of practical devices that acquire frontal-and-one precordial lead ECGs with an acceptable degree of comfortableness [ 48 ], namely those patch-based (e.g., Bittium OmegaSnap™ [ 49 ]) or contact-based textile (e.g., Viscero ECG vest [ 50 ]) ECG electrodes.…”