Automated Off-Line Respiratory Event Detection for the Study of Postoperative Apnea in Infants
Bibliographic record
Abstract
Previously, we presented automated methods for thoraco-abdominal asynchrony estimation and movement artifact detection in respiratory inductance plethysmography (RIP) signals. This paper combines and improves these methods to give a method for the automated, off-line detection of pause, movement artifact, and asynchrony. Simulation studies demonstrated that the new combined method is accurate and robust in the presence of noise. The new procedure was successfully applied to cardiorespiratory signals acquired postoperatively from infants in the recovery room. A comparison of the events detected with the automated method to those visually scored by an expert clinician demonstrated a higher agreement (κ = 0.52) than that amongst several human scorers (κ = 0.31) in a clinical study . The method provides the following advantages: first, it is fully automated; second, it is more efficient than visual scoring; third, the analysis is repeatable and standardized; fourth, it provides greater agreement with an expert scorer compared to the agreement between trained scorers; fifth, it is amenable to online detection; and lastly, it is applicable to uncalibrated RIP signals. Examples of applications include respiratory monitoring of postsurgical patients and sleep studies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".