Cuffless Blood Pressure Estimation Using Cardiovascular Dynamics
Bibliographic record
Abstract
Noninvasive estimation of blood pressure is important in preventing and managing cardiovascular disease. The cuffless technique has captured a lot of attention in recent years to unobtrusively provide continuous monitoring of blood pressure. This paper proposes a calibration-free approach, which makes use of dynamic changes of the pulse waveform over brief time intervals. Experimental results from two studies, the first using normalized intra-arterial blood pressure waveforms from 390 patients and the second using photoplethysmogram (PPG) waveforms from 200 patients, show that the accuracy of the proposed method for estimating the diastolic blood pressure (DBP) falls within the accuracy criteria of the Association for Advancement of Medical Instrumentation/European Society of Hypertension/International organization for Standardization (AAMI/ESH/ISO) standard and achieves grade B based on the British Hypertension Society (BHS) standard. In addition, a correlation of 99.4% was found between the inter-beat intervals (IBIs) obtained from the PPG and the intra-arterial waveforms in 390 patients. This paper demonstrates the possibility of using the PPG signal for estimation of blood pressure based on analysis of dynamic changes in the IBI.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".