Assessing Pulse Rate Variability from a Wrist-Worn PPG Device Against ECG-Derived Heart Rate Variability in Ambulatory Settings

Abstract

This study systematically evaluates the consistency and applicability limits of photoplethysmography (PPG)-derived pulse rate variability (PRV) versus electrocardiogram (ECG)-derived heart rate variability (HRV) in real-world settings. It integrates three methodological dimensions: 24-hour multi-context monitoring, dual-level consistency analysis (inter- and intra-individual), and controlled motion intensity via a 27-level acceleration gradient. Data from 14 healthy participants were collected using synchronized wrist-worn PPG, portable ECG, and triaxial accelerometry. Standardized preprocessing and motion artifact suppression based on acceleration thresholds enabled the extraction of time-domain, frequency-domain, and nonlinear HRV and PRV metrics. Consistency was assessed using Pearson correlation and root mean square error, with false discovery rate-corrected significance testing. Results show strong PPG-ECG agreement during sleep (r > 0.9 for HR, MeanNN, Prc80NN) but marked degradation under high motion. Notably, Prc20NN demonstrated exceptional robustness across contexts, retaining significant correlation even during active phases. Stringent motion filtering substantially improved correlations. These findings delineate metric-specific validity boundaries for wearable PRV, distinguishing motion-induced errors from inherent physiological discrepancies, and offer evidence-based recommendations for deploying PRV in context-appropriate applications such as sleep monitoring, passive health tracking, and longitudinal stress assessment.

Received: 15 October 2025 | Revised: 2 December 2025 | Accepted: 15 December 2025

Conflicts of Interest

Dan Zhang is the Editorial Board Member for Smart Wearable Technology and was not involved in the editorial review or the decision to publish this article. The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

To protect participant privacy while supporting scientific transparency, anonymized raw physiological signals (ECG, PPG, and accelerometer) and derived HRV/PRV features from five representative participants have been deposited and are publicly accessible at https://cloud.tsinghua.edu.cn/d/9629ab21aaba4f03a70d/.

Author Contribution Statement

Jie Huang: Methodology, Validation, Investigation, Writing – original draft, Writing – review & editing, Visualization. Yuhong Wu: Methodology, Validation, Data curation, Writing – original draft, Supervision. Fang Li: Validation, Data curation. Dan Zhang: Conceptualization, Methodology, Writing – review & editing, Project administration, Funding acquisition. Shuping Tan: Conceptualization, Writing – review & editing, Project administration.