Continuous blood pressure prediction from pulse transit time using ECG and PPG signals
Bibliographic record
Abstract
High blood pressure (BP) is the most common cause of death and disability in the world, and is the largest contributor to heart and kidney disease. Current methods of measuring and monitoring blood pressure require either invasive procedures or intermittent inflation of a cuff to restrict blood flow. Thus a non-invasive method for continuous blood pressure monitoring is needed. Pulse transit time (PTT), has been reported to be highly correlated with blood pressure but data examining the effect of posture and activity on PTT based BP estimation are very limited. In this paper, PTT was computed using the windowed correlation between ECG and PPG signals. Continuous blood pressure was estimated using a previously published linear regression model. In fourteen healthy subjects, BP was estimated using PTT in 5 different positions (recumbent, seated, standing, walking, cycling) for each subject according to a preset protocol. Accuracy was increased when sparsified, preprocessed PPG signals were used. Furthermore, the observed errors of PTT measurement were within 1% of manual PTT measurement. The Root-Mean-Squared Errors (RMSE) in systolic and diastolic blood pressure between the reference standard oscillometric cuff-based device and the estimated BP from PTT were lowest when seated or standing and highest when walking or cycling. The mean difference ±standard deviation (SD) of the difference between the PTT-based estimated systolic BP and the reference standard was 0.07±5.8 mmHg in the seated position; however, this increased to 4.4±20.9 and 10.2±16.0 when walking and cycling respectively. Therefore, PTT-based BP estimation was reasonably accurate while stationary but not during motion and further improvements in estimation are required before its use for the estimation of ambulatory BP.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".