Arterial Blood Pressure Estimation by Electrical Recording with a Vascular Cuff Electrode
Bibliographic record
Abstract
This thesis explores the potential of utilizing bioelectric signals, specifically electro-vasculargrams (EVG), to indicate blood flow and predict blood pressure (BP). The study involves a comprehensive analysis of EVG signals recorded from the carotid artery, demonstrating similarities to BP waveforms and capturing essential cardiovascular features like the dicrotic notch. By employing a specific data processing pipeline and convolutional neural network (CNN), the research successfully predicts BP using EVG signals alone, outperforming methods relying on electrocardiogram (ECG) and photoplethysmogram (PPG). The findings suggest that EVG has the potential for continuous BP monitoring without invasive arterial transducers, paving the way for closed-loop BP modulation systems. Despite some limitations, including hardware refinements and alternative modelsexploration, this research lays the groundwork for further advancements in EVG-based cardiovascular monitoring and personalized healthcare applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".