A Novel Method for Monitoring Data Quality in Electrical Impedance Tomography
Bibliographic record
Abstract
Electrical impedance tomography (EIT) has the promise to help improve care for patients undergoing ventilation therapy by providing real-time bed-side information on the distribution of ventilation in their lungs. To realise this potential, it is important for an EIT system to provide a reliable and meaningful signal at all times, or alert clinicians when this is not possible. Because the reconstructed images in EIT are sensitive to system instabilities (including electrode connection problems) and artifacts caused by e.g. movement or sweat, there is a need for EIT systems to continuously monitor, recognize and, if possible, correct for such errors. Motivated by this requirement, our paper describes a novel approach to quantitatively measure EIT data quality suitable for online and offline applications. We used a publicly available data set of ventilation data from two pediatric patients with lung disease to evaluate the data quality on clinical data. Results suggest that the developed data quality could be a useful tool for real-time assessment of the quality of EIT data and, hence, to indicate the reliability of any derived physiological information.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".