@Article{info:doi/10.2196/44642,
author="Selder, Jasper L
and Te Kolste, Henryk Jan
and Twisk, Jos
and Schijven, Marlies
and Gielen, Willem
and Allaart, Cornelis P",
title="Accuracy of a Standalone Atrial Fibrillation Detection Algorithm Added to a Popular Wristband and Smartwatch: Prospective Diagnostic Accuracy Study",
journal="J Med Internet Res",
year="2023",
month="May",
day="26",
volume="25",
pages="e44642",
keywords="smartwatch; atrial fibrillation; algorithm; fibrillation detection; wristband; diagnose; heart rhythm; cardioversion; environment; software algorithm; artificial intelligence; AI; electrocardiography; ECG; EKG",
abstract="Background: Silent paroxysmal atrial fibrillation (AF) may be difficult to diagnose, and AF burden is hard to establish. In contrast to conventional diagnostic devices, photoplethysmography (PPG)--driven smartwatches or wristbands allow for long-term continuous heart rhythm assessment. However, most smartwatches lack an integrated PPG-AF algorithm. Adding a standalone PPG-AF algorithm to these wrist devices might open new possibilities for AF screening and burden assessment. Objective: The aim of this study was to assess the accuracy of a well-known standalone PPG-AF detection algorithm added to a popular wristband and smartwatch, with regard to discriminating AF and sinus rhythm, in a group of patients with AF before and after cardioversion (CV). Methods: Consecutive consenting patients with AF admitted for CV in a large academic hospital in Amsterdam, the Netherlands, were asked to wear a Biostrap wristband or Fitbit Ionic smartwatch with Fibricheck algorithm add-on surrounding the procedure. A set of 1-min PPG measurements and 12-lead reference electrocardiograms was obtained before and after CV. Rhythm assessment by the PPG device-software combination was compared with the 12-lead electrocardiogram. Results: A total of 78 patients were included in the Biostrap-Fibricheck cohort (156 measurement sets) and 73 patients in the Fitbit-Fibricheck cohort (143 measurement sets). Of the measurement sets, 19/156 (12{\%}) and 7/143 (5{\%}), respectively, were not classifiable by the PPG algorithm due to bad quality. The diagnostic performance in terms of sensitivity, specificity, positive predictive value, negative predictive value, and accuracy was 98{\%}, 96{\%}, 96{\%}, 99{\%}, 97{\%}, and 97{\%}, 100{\%}, 100{\%}, 97{\%}, and 99{\%}, respectively, at an AF prevalence of {\textasciitilde}50{\%}. Conclusions: This study demonstrates that the addition of a well-known standalone PPG-AF detection algorithm to a popular PPG smartwatch and wristband without integrated algorithm yields a high accuracy for the detection of AF, with an acceptable unclassifiable rate, in a semicontrolled environment. ",
issn="1438-8871",
doi="10.2196/44642",
url="https://www.jmir.org/2023/1/e44642",
url="https://doi.org/10.2196/44642",
url="http://www.ncbi.nlm.nih.gov/pubmed/37234033"
}