Oscillometric blood pressure measurement: modeling and analysis of the area oscillogram and height oscillogram
Bibliographic record
Abstract
Objective: Oscillometry is the most popular blood pressure (BP) measurement method. Conventionally, BP is computed from the oscillation height versus cuff pressure function ("height oscillogram"). However, the oscillation shape also changes with cuff pressure. The objectives were to mathematically model oscillation shape and height variations as a function of cuff pressure and analyze these models using patient data. Methods: The patient data comprised oscillometric arm cuff pressure and invasive brachial BP waveforms from 109 patients with diverse BPs. The data were analyzed to show that the oscillation area versus cuff pressure function ("area oscillogram") in particular could be reliably constructed while offering distinct information to the height oscillogram. An analytical model of the area oscillogram was developed with four unknown parameters representing the widths of the brachial artery compliance curve over positive and negative transmural pressure ranges and systolic and diastolic BPs. With invasive systolic and diastolic BPs as inputs, this model and a previous height oscillogram model with the same four parameters, were evaluated in terms of fitting individual patient oscillograms. The impact of key assumptions of the models was evaluated as well. Results: The area and height oscillogram models fitted the patient data well with errors of 6.9% ± 0.3% and 8.7% ± 0.4%, respectively. Cuff-arm-artery viscoelasticity affected the height oscillogram model fitting, while cuff-arm system nonlinearity may affect area oscillogram model parameter estimates. Conclusion: Despite simplifying assumptions, the proposed area and previous height oscillogram models can reproduce measured patient oscillograms well. These models may ultimately help improve oscillometric BP measurement accuracy.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".