Upper airway resistance changes from inspiration to expiration during wakefulness is a predictor of sleep apnea: A pilot study
Bibliographic record
Abstract
Upper airway structure is known to be affected in individuals with obstructive sleep apnea (OSA). In this pilot study, we investigated whether the changes of upper airway resistance (UAR) within a breathing phase, estimated from tracheal breathing sounds and airflow, is a predictor of OSA severity. Tracheal breathing sounds and airflow of 15 individuals in three groups of mild, moderate and severe OSA during wakefulness, recorded in supine position, were used in this study. The grouping of the study subjects were done based on their apnea/hypopnea index (AHI) per hour, prospectively. The breathing sounds and airflow were recorded simultaneously with full overnight polysomnography (PSG) assessment. The sounds of 3 noise-free breathing cycles were extracted and sequestered into inspiratory and expiratory phase segments manually for each study subject. The power spectra of the sound signals of each respiratory phase were calculated in 15ms windows with 50% overlap between adjacent windows. The UAR was then estimated as the ratio of the energy of power spectrum of each window within 100 to 2500 Hz, and its corresponding airflow. Then, the variance of the short-time windows' UAR, Var(UAR) during each respiratory phase was calculated. The ratio of the Var(UAR) in logarithmic scale between inspiration and expiration was found to be significantly different (p<;0.01) between the three OSA groups; also it was found to be highly correlated with AHI. These results are congruent with the hypothesis that the upper airway patency is not kept constant in OSA population, and suggest the change in acoustic UAR during wakefulness maybe considered as a predictor of OSA.
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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 | 0.001 |
| 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".