A Bayesian Examination Of Equivalence Between Electrocardiogram-derived Heart Rate Variability And Photoplethysmogram-derived Heart Rate Variability
Bibliographic record
Abstract
Heart rate variability (HRV) is a common measure of autonomic nervous system function used in both clinical and performance settings. HRV is measured by detecting repeated QRS intervals via electrocardiography (ECG). Consumer wearables commonly report “HRV” metrics by using photoplethysmography (PPG) detected at the wrist or finger. The PPG signal is a measure of blood flow versus the electrical ECG signal and although the calculations for the variability indices are similar, the outcomes of PPG-derived HRV (termed PRV) may not be an equivalent measure. One potential cause of dissociation between HRV and PRV is the variability in pulse transit time (PTT; time between heartbeat and peripheral pulse). PURPOSE: To determine if PRV measured with PPG is equivalent to HRV. METHODS: The data used were from two separate publicly accessible sources. ECG data from 1,084 subjects were obtained from the PhysioNet Autonomic Aging dataset and individual PTT variances for both the wrist (n = 26) and finger (n = 29) were derived from Rajala et al (2018). A Bayesian simulation was constructed whereby the individual arrival times of the PPG wave were calculated by placing a Gaussian prior on the 1,084 ECG series. The standard deviation of the prior corresponds to the variances from Rajala et al (2018). This was simulated 10,000 times for each PTT variance (total 596,200,000 simulated PPG series). 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. RESULTS: Results of the ROPE analysis are shown in Figure 1. As the SD of PTT increases, the equivalence of PRV and HRV decreases for both SDNN (A) and RMSSD (B). CONCLUSION: To be deemed “equivalent,” PTT variance should be <1 SD and < 2.5 SD for RMSSD and SDNN, respectively. For individuals with greater PTT variability, PRV is not a surrogate for HRV .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.050 | 0.239 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".