Observation of blood motion in the internal jugular vein by contact and contactless photoplethysmography during physiological testing: case studies
Bibliographic record
Abstract
Central venous pressure is an estimate of right atrial pressure and is often used to assess hemodynamic status. However, since it is measured invasively, non-invasive alternatives would be of great utility. The aim of this preliminary study was a) to investigate whether photoplethysmography (PPG) can be used to characterize venous system fluid motion and b) to find the model for venous blood volume modulations. For this purpose, we monitored the internal jugular veins using contact (cPPG) and video PPG during clinically validated physiological tests: abdominojugular test (AJT) and breath holding (BH). Video PPG and cPPG signals were captured simultaneously on the left and right sides of the neck, respectively. ECG was also captured using the same clinical monitor as cPPG. Two volunteers underwent AJT and BH with head up/down, each with: baseline (15s), experiment (15s), and recovery (15s). Video PPG was split into remote PPG (rPPG) and micromotion detection. All signal modalities were significantly affected by physiological testing. Moreover, cPPG and micromotion waveforms exhibited primary features of jugular vein waveforms and, therefore, have great potential for venous blood flow monitoring. Specifically, remote patient monitoring applications may be enabled by this methodology, facilitating physical collection without a specially trained care provider.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".