Limitations and challenges of EIT-based monitoring of stroke volume and pulmonary artery pressure
Bibliographic record
Abstract
OBJECTIVE: Electrical impedance tomography (EIT) shows potential for radiation-free and noninvasive hemodynamic monitoring. However, many factors degrade the accuracy and repeatability of these measurements. Our goal is to estimate the impact of this variability on the EIT-based monitoring of two important central hemodynamic parameters: stroke volume (SV) and pulmonary artery pressure (PAP). APPROACH: We performed simulations on a 4D ([Formula: see text]) bioimpedance model of a human volunteer to study the influence of four potential confounding factors (electrode belt displacement, electrode detachment, changes in hematocrit and lung air volume) on the performance of EIT-based SV and PAP estimation. Results were used to estimate how these factors affect the EIT measures of either absolute values or relative changes (i.e. trending). MAIN RESULTS: Our findings reveal that the absolute measurement of SV via EIT is very sensitive to electrode belt displacements and lung conductivity changes. Nonetheless, the trending ability of SV EIT might be a promising alternative. The timing-based measurement of PAP is more robust to lung conductivity changes but sensitive to longitudinal belt displacements at severe hypertensive levels and to rotational displacements (independent of the PAP level). SIGNIFICANCE: We identify and quantify the challenges of EIT-based SV and PAP monitoring. Absolute SV via EIT is challenging, but trending is feasible, while both the absolute and trending of PAP via EIT are mostly impaired by belt displacements.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".