JMIR Publications

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/wristbands allow for long-term continuous heart rhythm assessment. However, most smartwatches lack an integrated PPG-AF algorithm. Adding a stand-alone PPG-AF algorithm to these wrist devices might open new possibilities for AF screening and burden assessment.

Objective:

To assess the accuracy of adding a standalone PPG-AF detection software algorithm to a popular wristband and smartwatch for detection of AF.

Methods:

Consecutive consenting patients with AF admitted for cardioversion (CV) in a large academic hospital in Amsterdam (NL) 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 ECGs were obtained before and after CV. Rhythm assessment by the PPG device-software combinations were compared with the 12-lead ECG.

Results:

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 (12%) and 7 (5%), respectively, were not classifiable by the PPG algorithm due to bad quality. The diagnostic performance (sensitivity/specificity/positive predictive value/negative predictive value/accuracy) was 98/96/96/99/97% and 97/100/100/97/99%, respectively, at an AF prevalence of ~50%.

Conclusions:

This study demonstrates that adding a standalone PPG-AF detection algorithm to PPG smartwatches/wristbands without integrated algorithm is feasible and yields a high accuracy for detection of AF, with an acceptable unclassifiable rate, in a semi-controlled environment.