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January 20, 2026npj Digital Medicine3 citationsOpen Access

Wearable device derived electrocardiographic age and its association with atrial fibrillation

SPSeung Hyun ParkJJJu Hyun JinJKJongwoo Kim

Key Result

Each 1-year increase in the AI-ECG age gap derived from wearable single-lead ECGs was associated with a 3% higher odds of prevalent atrial fibrillation (OR 1.03).

Key Points

  • To investigate the relationship between AI-derived electrocardiographic age and atrial fibrillation risk.
  • Developed AI model PROPHECG-Age Single for estimating ECG age from single-lead wearable ECGs.
  • Trained the model using synthetic signals converted from 12-lead ECGs.
  • Validated the model in two independent wearable cohorts.
  • Mean absolute errors in ECG age predictions were 10.01 and 11.88 years across cohorts.
  • Each 1-year ECG age gap correlates with a 1.03 odds ratio for atrial fibrillation presence.
  • A 1-year increase in ECG age gap is linked to a 0.8 percentage point rise in AF burden.

Study Design

Type

Cross-Sectional (n=2,031)

Multicenter

Yes

Structured PICO

Does the AI-derived ECG-age gap from wearable single-lead ECGs associate with atrial fibrillation presence and burden in adults?

P
Population
Adults aged 20-90 years undergoing wearable single-lead ECG monitoring. The study included a training cohort of 1,008,566 12-lead ECGs from Severance Hospital, an internal validation cohort of 1,502 participants from the S-Patch registry, and an external validation cohort of 529 participants from the Memo Patch registry.
I
Intervention
PROPHECG-Age Single, an artificial intelligence model estimating ECG age from wearable single-lead ECGs to calculate the ECG-age gap (predicted minus chronological age).
C
Comparator
Chronological age (for age gap calculation) and participants without atrial fibrillation.
O
Outcome
Association of the AI-ECG age gap with atrial fibrillation presence and burden.surrogate

An AI model estimating ECG age from wearable single-lead ECGs demonstrated that a higher ECG-age gap is significantly associated with increased atrial fibrillation presence and burden, highlighting its potential as a digital biomarker.

Main Result

Effect estimate: OR 1.03 (95% CI 1.01-1.04)

Limitations

  • High mean absolute error (≈10–12 years) due to structural information loss inherent to single-lead settings
  • Cross-sectional design cannot establish causality or temporality
  • Quasi-episodic high-frequency sampling rather than real-time, beat-to-beat continuous tracking
  • Small sample size for the AF burden analysis in the external cohort
  • Single East Asian ethnicity limits generalizability to other populations
  • Lack of direct comparison between single-lead and standard 12-lead AI-ECG age gap within the same participants
  • Lack of uncertainty estimates limits the ability to differentiate true physiological variability from model prediction error

Abstract

Artificial intelligence (AI)-derived electrocardiographic (ECG) age is a promising marker of atrial fibrillation (AF) risk. We developed PROPHECG-Age Single-an AI model estimating ECG age from wearable single-lead ECGs-and examined whether the ECG-age gap (predicted minus chronological age) is associated with AF presence and burden in real-world self-monitoring context. One million 12-lead ECGs from a hospital were converted to synthetic single-lead signals via Cycle-Consistent Generative Adversarial Network and used to train a residual network-based model. Validation in two independent wearable cohorts (S-Patch ClinicalTrials.gov: NCT05119725, registered November 2021; Memo Patch ClinicalTrials.gov: NCT05355948, registered May 2022) showed mean absolute errors of 10.01 and 11.88 years, respectively. The pooled association with AF presence was significant (odds ratio 1.03 per 1-year gap), and for AF burden, each 1-year gap increase corresponded to a 0.8 percentage point rise. These findings support wearable-based AI-ECG age as a potential digital biomarker for proactive cardiovascular monitoring.

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Cite This Study

Park et al. (2026) conducted a cross-sectional in Atrial fibrillation (n=2,031). PROPHECG-Age Single (AI-ECG age gap) was evaluated on Prevalent atrial fibrillation (OR 1.03, 95% CI 1.01-1.04). Each 1-year increase in the AI-ECG age gap derived from wearable single-lead ECGs was associated with a 3% higher odds of prevalent atrial fibrillation (OR 1.03).

synapsesocial.com/papers/696f1ac19e64f732b51ef0b5https://doi.org/10.1038/s41746-026-02344-8
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