Signal Quality Enhancement of Esophageal Pressure Signals in Mechanically Ventilated Patients
Bibliographic record
Abstract
The Esophageal Pressure (Peso) signal can be used to monitor the respiratory mechanics of critically ill patients in the Intensive Care Unit (ICU), and has been successfully used in guiding mechanical ventilation strategies to improve patient outcomes. However, cardiogenic oscillations (CGOs) are a major source of interference, which not only makes it challenging in interpreting the patient’s respiratory mechanics, but can also cause false triggers in the mechanical ventilator resulting in a patient-ventilator asynchrony. In this thesis, we present a Peso enhancement scheme using Ensemble Empirical Mode Decomposition (EEMD) to suppress CGO interference. The proposed method was applied to synthetically generated Peso signals as well as real-world Peso signals from mechanically ventilated ICU patients. The proposed technique has been shown to significantly reduce the amplitude fluctuations caused by CGOs. The technique’s performance has been assessed through Face Validation by our collaborating clinicians, and is found to be suitable in not only suppressing CGO, but also extracting CGO from clinically acquired Peso signals.
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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 | 0.000 | 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".