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February 8, 2026European Heart Journal

PPG-based smartwatch AI detects AF with ~92% sensitivity and strongly correlates with ECG Holter duration.

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Why the study?

AF burden is typically measured only as percentage time in AF, but technological innovations may allow multi-dimensional precise AF progression estimation in daily life.

Does a PPG-based smartwatch with an AI model accurately assess atrial fibrillation progression compared to a 24-hour Holter monitor in patients with paroxysmal AF?

Comparison

PPG-based smartwatch with PPG-AF AI model vs 24-hour Holter

Design

Prospective cohort study

Key result

PPG-based smartwatch AI model detected AF with 91.5% sensitivity, 97.2% specificity, and strongly correlated AF duration (r=0.92) to ECG Holter data.

Authors

YGY GuoHWH O N G WangHWH O N G Wang

Discussion

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Overview

Supports PPG smartwatch AI for AF assessment in paroxysmal AF; leaves open whether it improves outcomes versus Holter before clinical adoption.

Key Points

  • To develop a quantitative method for assessing atrial fibrillation progression using a PPG-based smartwatch.
  • Prospective cohort study with patients having paroxysmal AF monitored using PPG and Holter systems.
  • PPG data recorded every minute; a minimum of 500 quality readings required for analysis.
  • Developed PPG-AF AI model for AF detection, focusing on minimizing motion artifacts and detecting key PPG features.
  • Evaluated five AF progression features: number and duration of AF episodes, aggregation, circadian rhythm, and heart rate.
  • Total of 145 patients identified with 116 AF episodes.
  • Strong correlation for AF duration between PPG-AF model and ECG (r=0.92, p<0.0001).
  • Circadian rhythm feature showed high correlation (average real variability=0.81, p<0.0001).
  • Moderate correlation for number and aggregation features (r=0.709 and r=0.6576 respectively, all p<0.0001).
  • PPG-AF AI model had an output rate of 86.7%, with sensitivity, specificity, and accuracy at 91.5%, 97.2%, and 95.7% respectively.

Structured PICO

Does a PPG-based smartwatch with an AI model accurately assess atrial fibrillation progression compared to a 24-hour Holter monitor in patients with paroxysmal AF?

P
Population
145 patients with paroxysmal AF, mean age 63±14 years, 96 male, from a single center in China. Inclusion required ≥ 500 PPG recordings with adequate signal quality.
I
Intervention
PPG-based smartwatch with a PPG-AF AI model for multi-dimensional quantitative assessment of AF progression (measuring number, duration, aggregation, circadian rhythm, and heart rate)
C
Comparator
24-hour Holter monitor (simultaneous monitoring)
O
Outcome
Correlation between AF progression features detected by PPG-smart watch and 24-hour Holtersurrogate

A PPG-based smartwatch AI model provides reliable and valid multi-dimensional assessment of atrial fibrillation progression compared to standard 24-hour Holter monitoring.

Cite This Study

Guo et al. (2025) studied this question. PPG-based smartwatch AI model detected AF with 91.5% sensitivity, 97.2% specificity, and strongly correlated AF duration (r=0.92) to ECG Holter data.

synapsesocial.com/papers/698828770fc35cd7a8847fedhttps://doi.org/10.1093/eurheartj/ehaf784.772
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