Acoustical flow estimation in patients with obstructive sleep apnea during Sleep
Bibliographic record
Abstract
Tracheal respiratory sound analysis is a simple and non-invasive way to study the pathophysiology of the upper airways; it has recently been used for acoustical flow estimation and sleep apnea diagnosis. However in none of the previous studies, the accuracy of acoustical flow estimation was investigated neither during sleep nor in people with obstructive sleep apnea (OSA). In this study, we recorded tracheal sound, flow rate and head position from 11 individuals with OSA during sleep and wakefulness. We investigated two approaches for calibrating the parameters of acoustical flow estimation model based on the known data recorded during wakefulness and sleep. The results show that the acoustical flow estimation parameters change from wakefulness to sleep. Therefore, if the model is calibrated based on the data recorded during wakefulness, although the estimated flow follows the relative variations of the recorded flow, the quantitative flow estimation error would be high during sleep. On the other hand, when the calibration parameters are extracted from tracheal sound and flow recordings during sleep, the flow estimation error is less than 5%. These results confirm the reliability of acoustical methods for estimating breathing flow during sleep and detecting the partial or complete obstructions of the upper airways during sleep.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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 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".