Are Wearable Photoplethysmogram-Based Heart Rate Variability Measures Equivalent to Electrocardiogram? A Simulation Study

• Published in: Sports medicine (Auckland) Vol. 54; no. 11; pp. 2927 - 2934
• Main Authors: Dewig, Hayden G., Cohen, Jeremy N., Renaghan, Eric J., Leary, Miriam E., Leary, Brian K., Au, Jason S., Tenan, Matthew S.
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.11.2024; Springer Nature B.V
• Subjects: Adult; Aged; Aging; Bayes Theorem; Bayesian analysis; Biomarkers; Biometrics; Blood; Computer Simulation; EKG; Electrocardiography; Female; Fingers - blood supply; Fingers - physiology; Heart Rate; Humans; Male; Measurement errors; Medicine; Medicine & Public Health; Older people; Original Research Article; Photoplethysmography - methods; Physiology; Simulation; Sports Medicine; Standard deviation; Wearable Electronic Devices; Wrist; Young adults
• ISSN: 0112-1642; 1179-2035; 1179-2035
• DOI: 10.1007/s40279-024-02066-5

Background Traditional electrocardiography (ECG)-derived heart rate variability (HRV) and photoplethysmography (PPG)-derived “HRV” (termed PRV) have been reported interchangeably. Any potential dissociation between HRV and PRV could be due to the variability in pulse arrival time (PAT; time between heartbeat and peripheral pulse). Objective This study examined if PRV is equivalent to ECG-derived HRV and if PRV’s innate error makes it a high-quality measurement separate from HRV. Methods ECG data from 1084 subjects were obtained from the PhysioNet Autonomic Aging dataset, and individual PAT dispersions for both the wrist ( n  = 42) and finger ( n  = 49) were derived from Mol et al. (Exp Gerontol. 2020; 135: 110938). A Bayesian simulation was constructed whereby the individual arrival times of the PPG wave were calculated by placing a Gaussian prior on the individual QRS-wave timings of each ECG series. The standard deviation (σ) of the prior corresponds to the PAT dispersion from Mol et al. This was simulated 10,000 times for each PAT σ. The root mean square of successive differences (RMSSD) and standard deviation of N–N intervals (SDNN) were calculated for both HRV and PRV. The Region of Practical Equivalence bounds (ROPE) were set a priori at ± 0.2% of true HRV. The highest density interval (HDI) width, encompassing 95% of the posterior distribution, was calculated for each PAT σ. Results The lowest PAT σ (2.0 SD) corresponded to 88.4% within ROPE for SDNN and 21.4% for RMSSD. As the σ of PAT increases, the equivalence of PRV and HRV decreases for both SDNN and RMSSD. The HDI interval width increases with increasing PAT σ, with the HDI width increasing at a higher rate for RMSSD than SDNN. Conclusions For individuals with greater PAT variability, PRV is not a surrogate for HRV. When considering PRV as a unique biometric measure, SDNN may have more favorable measurement properties than RMSSD, though both exhibit a non-uniform measurement error.