Compensating Electrode Errors Due to Electrode Detachment in Electrical Impedance Tomography
Bibliographic record
Abstract
Electrical impedance tomography (EIT) shows a great promise for monitoring pul- monary and cardiovascular functions non-invasively. However, there are some challenges to bring EIT from the laboratory to daily clinical use in intensive care unit (ICU). One of the main challenges is the measurement errors caused by poor contact or de- tachment of electrodes due to the dynamics of en- vironment and human body. Such errors create large image artifacts and may even lead to misleading re- sults. Thus, there is a need for unsupervised failing electrode identification and electrode error compen- sation. We developed a novel formulation to compen- sate for such errors caused by failing electrodes and to eliminate image artifacts in real-time. We tested the error correction algorithms with measurements acquired on a cylindrical tank filled with a conduc- tive saline solution. A test object was placed at differ- ent positions inside the tank using a robotic system. For each position, several combinations of discon- nected electrodes were tested. The developed algo- rithm - evaluated by comparing the known test object with the reconstructed images - reduced image arti- facts caused by failing electrodes and thus improved the robustness of EIT measurements. The results also demonstrated that the proposed failing electrode compensation strategy was effective up to 6 discon- nected electrodes for a 32-electrode EIT system. The proposed strategy can help to use EIT as a practical and robust bedside imaging technique for ventilation monitoring.
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 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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".