February 4, 2022Frontiers in Cardiovascular MedicineOpen Access

Smartwatch Electrocardiograms for Automated and Manual Diagnosis of Atrial Fibrillation: A Comparative Analysis of Three Models

View Full Paper

Ask AI

Bookmark

Why the study?

The diagnostic accuracy of proprietary smartwatch algorithms and the interpretability of smartwatch ECG tracings may differ between available models.

Do different smartwatch models vary in their diagnostic accuracy for detecting atrial fibrillation?

Structured PICO

Do different smartwatch models vary in their diagnostic accuracy for detecting atrial fibrillation?

Population

200 patients (100 in atrial fibrillation and 100 in sinus rhythm; patients with atrial flutter excluded)

Intervention

ECG recordings using three smartwatch models (Apple Watch Series 5, Samsung Galaxy Watch Active 3, and Withings Move ECG) analyzed by automated software and clinical experts

Comparator

Conventional 12-lead ECG

Outcome

Diagnostic accuracy (sensitivity and specificity) for detecting atrial fibrillationsurrogate

Different smartwatch models demonstrate varying diagnostic accuracy for atrial fibrillation detection, with differences observed between automated algorithms and expert manual interpretation.

Implication

Accuracy varies by smartwatch model for AF detection; leaves open optimal device selection for routine use.

Abstract

Aims The diagnostic accuracy of proprietary smartwatch algorithms and the interpretability of smartwatch ECG tracings may differ between available models. We compared the diagnostic potential for detecting atrial fibrillation (AF) of three commercially available smartwatches. Methods We performed a prospective, non-randomized, and adjudicator-blinded clinical study of 100 patients in AF and 100 patients in sinus rhythm, patients with atrial flutter were excluded. All patients underwent 4 ECG recordings: a conventional 12-lead ECG, Apple Watch Series 5®, Samsung Galaxy Watch Active 3®, and Withings Move ECG® in random order. All smartwatch ECGs were analyzed using their respective automated proprietary software and by clinical experts who also graded the quality of the tracings. Results The accuracy of automated AF diagnoses by Apple and Samsung outperformed that of Withings, which was attributable to a higher proportion of inconclusive ECGs with the latter (sensitivity/specificity: 87%/86% and 88%/81% vs. 78%/80%, respectively, p < 0.05). Expert interpretation was more accurate for Withings and Apple than for Samsung (sensitivity/specificity: 96%/86% and 94%/84% vs. 86%/76%, p < 0.05), driven by the high proportion of uninterpretable tracings with the latter (2 and 4% vs. 15%, p < 0.05). Conclusion Diagnosing AF is possible using various smartwatch models. However, the diagnostic accuracy of their automated interpretations varies between models as does the quality of ECG tracings recorded for manual interpretation.