Mathematical modeling and parameter estimation of blood pressure oscillometric waveform
Bibliographic record
Abstract
In this paper, a mathematical model for the blood pressure oscillometric waveform (OMW) is developed and a statespace approach using the extended Kalman filter (EKF) is proposed to adaptively estimate and track parameters of clinical interest. The OMW model is driven by a previously proposed pressure-lumen area model of the artery under the deflating cuff. The arterial lumen area is a function of vessel properties, the cuff pressure, and the arterial pressure. Over the deflation period, the arterial pressure causes lumen area oscillations while the deflating cuff pressure adds a slow-varying component to these oscillations. In the previous literature, it has been demonstrated that the oscillometric pulses are proportional to the arterial area oscillations. In this paper, the OMW is modeled as the difference between the whole lumen area model and the slow-varying component of the lumen area caused by the deflating cuff pressure. The OMW model is then represented in the statespace and the extended Kalman filter (EKF) is incorporated to estimate and track the time-varying model parameters during the cuff deflation period. The parameter tracking performance of the EKF is evaluated on a simulated noisy OMW.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.001 |
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".