Electrocardiogram-Assisted Blood Pressure Estimation
Bibliographic record
Abstract
Accurate automatic noninvasive assessment of blood pressure (BP) presents a challenge due to conditions like arrhythmias, obesity, and postural changes that tend to obfuscate arterial amplitude pulsations sensed by the cuff. Researchers tried to overcome this challenge by analyzing oscillometric pulses with the aid of a higher fidelity signal-the electrocardiogram (ECG). Moreover, pulse transit time (PTT) was employed to provide an additional method for BP estimation. However, these methods were not fully developed, suitably integrated, or tested. To address these issues, we present a novel method whereby ECG-assisted oscillometric and PTT (measured between ECG R-peaks and maximum slope of arterial pulse peaks) analyses are seamlessly integrated into the oscillometric BP measurement paradigm. The method bolsters oscillometric analysis (amplitude modulation) with more reliable ECG R-peaks provides a complementary measure with PTT analysis (temporal modulation) and fuses this information for robust BP estimation. We have integrated this technology into a prototype that comprises a BP cuff with an embedded conductive fabric ECG electrode, associated hardware, and algorithms. A pilot study has been undertaken on ten healthy subjects (150 recordings) to validate the performance of our prototype against United States Food and Drug Administration approved Omron oscillometric monitor (HEM-790IT). Our prototype achieves mean absolute difference of less than 5 mmHg and grade A as per the British Hypertension Society protocol for estimating BP, with the reference Omron monitor.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".