Multi-dimensional quantitative assessment approach of atrial fibrillation progression with photoplethysmograph-based smart watch

The burden of atrial fibrillation (AF) is usually as the percentage of time in AF. Nonetheless , the technological innovations may enable multi-dimensional precise AF progression estimation in daily life.

To develop a quantitative approach for understanding the patterns of AF progression with photoplethysmography (PPG)-based smart watch.

In this prospective cohort study,the patients with paroxysma l AF were enrolled between

March 1, 2024 to January 20, 2025 from sixth Medical center General Hospital China. All patients were simultaneously monitored cardiac rhythm with PPG-based smartwatch and 24-hour Holter. The pulse rhythm was measured every one minute with PPG-based smart watch. The patients with ≥ 500 PPG recordings with adequate signal quality were included in the analysis.

We developed a PPG-AF AI model using time-series PPG data to accurately catch up the robust PPG feature for efficient AF detection through minimizing motion artifacts and the premature atrial and ventricular contractions. Then multi-dimensional quantitative assessment approach of AF progression with PPG- AF model was developed consisted of five features of AF progression (Figure 1):1) Number of AF episodes;2) Duration of AF episodes;3) AF aggregation;4) circadian rhythm;5) Heart rate.

The primary endpoint was the correlation between AF progression features detected by PPG-smart watch and 24-hour Holter. Secondary outcome was the sensitivity specificity accuracy and output rate of PPG-AF AI model.

One hundred and forty-five patients (96 male, age, mean±SD, 63±14 years) detected with total 116 AF episodes.

The correlation between Duration feature detected with PPG-AF AI model and ECG was strong (AF lasting over 6 mins, spearman correlation coefficient, r=0.92, p < 0.0001). There was a close correlation of circadian rhythm feature between PPG-AF AI model and ECG (e.g, average real variability=0.81, p < 0.0001). Number feature and Aggregation feature of PPG-AF AI model moderately correlated with ECG (AF episodes over 1 hour, r=0.709; AF density, r=0.6576, all p <0.0001) (Figure 2A-2E) . AF burden by PPG-AF AI model and ECG was consistent for 24 hours (Figure 2F).

The output rate of PPG-AF AI model with was 86.7%. The sensitivity, specificity, and accuracy (%, CI) of AF detection were 0.915 [0.879, 0.951], 0.972[0.959,0.985], and 0.957[0.940, 0.974], compared to 24-hour Holter.

Multi-dimensional quantitative assessment approach with PPG based smartwatch has good reliability and validity in the evaluation of AF progression, especially in the Duration, Circadian rhythm, Number and Aggregation of AF events, and it can better evaluate the AF burden in daily life.