Electrocardiogram and Photoplethysmogram-based Heart Rate Variability Are Not Equivalent: A Bayesian Simulation Analysis

BackgroundHeart rate variability (HRV) is a common measure of autonomic and cardiovascular system function assessed via electrocardiography (ECG). Consumer wearables, commonly employed in epidemiological research, use photoplethysmography (PPG) to report HRV metrics (PRV), although these may not be equivalent. One potential cause of dissociation between HRV and PRV is the variability in pulse transit time (PTT). This study sought to determine if PPG-derived HRV (i.e., PRV) is equivalent to ECG-derived HRV and ascertain if PRV measurement error is sufficient for a biomarker separate from HRV. MethodsThe ECG data from 1,084 subjects were obtained from the PhysioNet Autonomic Aging dataset, and individual PTT variances for both the wrist (n=42) and finger (n=49) were derived from Mol et al. 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 of the prior corresponds to the PTT variances. This was simulated 10,000 times for each PTT variance. 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 PTT variance. ResultsThe lowest PTT variance (2.0 SD) corresponded to 88.4% within ROPE for SDNN and 21.4% for RMSSD. As the SD of PTT increases, the equivalence of PRV and HRV decreases for both SDNN and RMSSD. Thus, between PRV and HRV, RMSSD is nearly never equivalent and SDNN is only somewhat equivalent under very strict circumstances. The HDI interval width increases with increasing PTT variance, with the HDI width increasing at a higher rate for RMSSD than SDNN. ConclusionsFor individuals with greater PTT variability, PRV is not a surrogate for HRV. When considering PRV as a unique biometric measure, our findings reveal that SDNN has more favorable measurement properties than RMSSD, though both exhibit a non-uniform measurement error.