Feasibility and Interest of Continuous Diaphragmatic Fatigue Monitoring Using Wavelet Denoising in ICU and Anesthesia
Bibliographic record
Abstract
Measures of diaphragmatic electromyography (Edi), and respiratory mechanics, have demonstrated early changes before clinical complications. However, automatic Edi data collection is not adequate today due mainly to severe artefacts as well as to loss of signal. We thus intended to develop a new device with embedded artificial intelligence to optimize automatic Edi recordings independantly of artefacts and of probe displacement. We first chose the best mathematical tool to denoise Edi, using an established database, giving multiresolution wavelets as the best, resulting in the permanent availability of the H/L spectral index, a recognized representative of diaphragmatic fatigue. Fatigue was simultaneously measured using the classical mechanical f/Vt index (Rapid Shallow Breathing Index, RSBI), as well as the transdiaphragmatic pressure. We then performed a comparison of real-time H/L and RSBI in a group of seven healthy volunteers, before and during midazolam sedation infusion 0.1 mg.kg-1, with a parallel CPAP administration (2.5, 5.0, and 10 cm H2O) intended to compensate for airways resistance due to midazolam. Procedure was ended by delivering the antagonistic flumazenil 0.2 to 0.5 mg.kg-1. Progressive fatigue due to midazolam, the relief due to CPAP, as well as the answer to the anatgonist flumazenil, were shown earlier by the H/L index than by the RSBI change. Our new H/L monitoring device may greatly improve clinical follow-up of anesthetized patients as well as help to determine the optimal period for ventilatory weaning in ICU (Clinical Trials NCT00133939).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".