Beat-to-Beat Oscillometric Blood Pressure Estimation: A Bayesian Approach With System Identification
Bibliographic record
Abstract
OBJECTIVE: Our study aims to advance noninvasive blood pressure (BP) monitoring through the introduction of innovative beat-to-beat oscillometric BP estimation methods. We aim to overcome current device limitations by delivering continuous and accurate BP estimates, utilizing physiologically based mathematical models. METHODS: We developed novel beat-to-beat oscillometric BP estimation methods based on physiologically grounded mathematical models of intra-arterial BP and the arterial system effect. Our approach includes a recursive Bayesian method for parameter estimation and a new system identification technique to refine initial parameter estimates. We tested our methods through simulations and real-world experiments involving 10 individuals. RESULTS: Mean errors for systolic and diastolic BP were as low as -1.26 mmHg and 2.03 mmHg, respectively, with standard deviations of errors at 5.95 mmHg and 4.16 mmHg. Furthermore, our methods enabled the estimation of additional cardiovascular parameters such as heart rate, respiration rate, and mean arterial pressure. CONCLUSION: Our novel beat-to-beat oscillometric BP estimation methods offer a significant advancement in noninvasive BP monitoring technology, addressing the limitations of current devices by providing continuous beat-to-beat BP estimates. SIGNIFICANCE: Our approach represents a promising direction for improving the reliability and comprehensiveness of cardiovascular parameter estimation in noninvasive BP monitoring devices, facilitating more effective patient care and monitoring.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.002 |
| 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".