Distinguishing atrial fibrillation from sinus rhythm using commercial pulse detection systems: The non-interventional BAYathlon study

Abstract

Objective: Early diagnosis of atrial fibrillation (AFib) is a priority for stroke prevention. We sought to test four commercial pulse detection systems (CPDSs) for ability to distinguish AFib from normal sinus rhythm using a published algorithm (Zhou et al., PLoS One 2015;10:e0136544), compared with visual diagnosis by electrocardiogram inspection.

Methods: BAYathlon was a prospective, non-interventional, single-centre study. Adult cardiology patients with documented AFib or sinus rhythm who were due to have a routine 5-min electrocardiogram were randomized to undergo a parallel 5-min pulse assessment with a Polar V800, eMotion Faros 360, TomTom heart rate monitor, or Adidas miCoach Smart Run.

Results: 144 patients (73 with AFib, 71 with sinus rhythm (based on electrocardiograms); median age: 73 years; 53.5% male) were analysed. Algorithm sensitivities (primary endpoint) and specificities for AFib when applied to CPDS recordings were 93.3% and 94.1% with the Polar V800, 90.0% and 84.2% with the eMotion Faros 360, and 0% and 100% with the other CPDSs (analysis period: 127 heart rate signals + 2 min). When applied to routine electrocardiograms, the algorithm correctly detected AFib in 71/73 patients. Different analysis periods (127 heart rate signals +1 or 3 min) only slightly changed the sensitivities with the Polar V800 and eMotion Faros 360 and had no effect on the sensitivities with the other CPDSs.

Conclusion: AFib screening using the applied algorithm is feasible with the Polar V800 and eMotion Faros 360 (which provide RR interval data) but not with the other CPDSs (which provide pre-processed heart rate time series).ClinicalTrials.gov identifier: NCT02875106.

Keywords: Atrial fibrillation; algorithms; electrocardiography; observational study; sensitivity; sinus rhythm.

Conflict of interest statement

Declaration of conflicting interests: CM and MK are employees of Bayer Vital GmbH (Leverkusen, Germany). UH is an employee of Bayer AG (Leverkusen, Germany). CM and UH are inventors for a patent application of the BAYathlon app on behalf of Bayer AG (not yet granted). MF has no relationships relevant to the content of this paper to disclose. FK is an employee of Bayer AG (Berlin, Germany). SM received consultancy fees from Bayer Vital GmbH. HM and IB have no relationships with industry that might pose a conflict of interest in connection with the submitted article. SP is Editor-in-Chief of www.kardiologie.org which is partly sponsored by Bayer Vital GmbH.

© The Author(s) 2021.

Figures

Figure 1.

Parallel assessment by routine electrocardiogram and a commercial pulse detection system. Patients underwent parallel assessment by routine electrocardiogram and a commercial pulse detection system (the Polar V800 in this example) for 5 minutes.

Figure 2.

Overview of data processing steps. Patients with AFib or sinus rhythm underwent parallel assessment by routine ECG and one of four commercial pulse detection systems. Data were analysed using the AFib detection algorithm of Zhou et al. aData from these devices are already delivered as HR time series. bMoving average RR intervals over different window sizes were calculated from Polar V800 and eMotion Faros 360 data in patients with AFib (diagnosed by the investigator based on the routine ECG) to explore the effect of data pre-processing on the function of the algorithm. AFib: atrial fibrillation; ECG: electrocardiogram; HR: heart rate; HRM: heart rate monitor; OSEA-4-Java: open source ECG analysis software for Java (Version 1.0.0, https://github.com/MEDEVIT/OSEA-4-Java. ); tRR: RR intervals.

Figure 3.

Patient disposition. Of 163 patients who were screened, 161 were enrolled and 144 were analysed. aRoutine ECG data obtained during the study were visually reviewed by the investigator to determine a diagnosis of AFib or sinus rhythm. AFib: atrial fibrillation; CPDS: commercial pulse detection system; ECG: electrocardiogram.

Figure 4.

(a)–(b) ECGs from two patients with AFib who were incorrectly classified by the algorithm based on their routine ECG data, and (c) a Poincaré plot of correctly detected AFib. The ECGs and Poincaré plots of the two incorrectly classified patients showed (a) and (d) pseudo-arrhythmic AFib and (b) and (e) AFib with partly regular transition. AFib: atrial fibrillation; ECG: electrocardiogram.