Evaluation of Pulse Arrival Time Model to Estimate Systolic Blood Pressure during Exercise
Bibliographic record
Abstract
Pulse arrival time (PAT) is a method used to estimate systolic blood pressure (SBP), as the amount of time it takes for a pulse to travel from the heart to a peripheral location is inversely related to blood pressure. However, the validity and accuracy of this method’s blood pressure estimation has been questioned. Therefore, the purpose of this study was to evaluate the accuracy of a PAT model’s estimation of SBP over a range of exercise intensities. Six participants (5 men, 1 woman; age: 26 ± 4 yrs) completed three cycling exercise tests (25 Watt/min ramp incremental test, and moderate and heavy pseudorandom binary sequence exercise) during three separate laboratory visits. PAT was calculated as the time difference between the R‐wave of an electrocardiogram and the pulse arrival at the forehead measured with a pulse oximeter. SBP was estimated using the following equation: SBP PAT = 64.578/(PAT) – 43.957, and was compared to calibrated brachial blood pressure measured at the finger (FBP) by photoplethysmography during the exercise trials. Participant blood pressure responses were pooled, and Bland‐Altman analysis was conducted to evaluate the accuracy of estimated SBP for each exercise test. PAT model estimation of SBP had the smallest bias during heavy (−2.8 mmHg), then moderate (8.1 mmHg), and the largest bias during ramp incremental exercise (−11 mmHg), with their limits of agreement being between −56 and 50 mmHg, −29 and 45 mmHg, and −74 and 53 mmHg, respectively. Linear regression of the ramp incremental SBP response revealed that SBP estimated from the PAT does not increase by the same magnitude as the pressure measured at the finger (SBP PAT = 0.295 · SBP FBP + 106.236, r 2 = 0.350, p < 0.05). Overall, these findings support that SBP estimated by PAT is linearly related to brachial SBP measured at the finger; however, this PAT model is a poor estimator of absolute SBP during exercise ranging from moderate to maximal intensities. Support or Funding Information Supported by NSERC.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".