An electrical pulse artifact signal for estimating arterial blood pressure: a proof-of-concept study
Bibliographic record
Abstract
Abstract Objective. Hypertension is a leading cause of mortality worldwide, for which myriad treatment options are available. It is widely considered that continuous measurement of arterial blood pressure (BP) could improve the treatment of hypertension; however, chronically monitoring patient BP remains a significant challenge. In this study, we investigated a novel approach that uses an implantable electrode to generate an artifact signal for predicting arterial BP. Approach. In isoflurane anesthetized rats ( n = 10, male), the right common carotid artery was instrumented with a multi-contact cuff electrode to acquire the artifact signal—termed the electro-vascular-gram (EVG) and the contralateral common carotid artery was catheterized to measure intra-arterial BP. The EVG signals were processed (e.g. extract Catch22 features) and applied to linear regression, random forest (RF) regressor, and convolutional neural network models to predict systolic and diastolic BP. Main results. Among the various models tested with the EVG data, the RF model + Catch22 features method achieved the highest performance, yielding predicted BP values (error < 5 mmHg) in 82.6%–100% and 84.1%–99.9% of the testing set for systolic and diastolic, respectively. A 5-fold cross-validation demonstrated similar performance by predicting BP values (error < 5 mmHg) in 91.5 ± 0.1% and 92.4 ± 0.1% of testing data for systolic and diastolic, respectively. Significance. This proof-of-concept study supports the feasibility of using an implantable electrode and machine learning models for potentially measuring arterial BP in continuous fashion. Further system development is warranted prior to clinical translation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".