Statistical analysis of tracheal breath sounds during wakefulness for screening obstructive sleep apnea
Bibliographic record
Abstract
Obstructive sleep apnea (OSA) is a prevalent disorder. The accepted method of diagnosis in widespread clinical practice, polysomnography (PSG), is costly and very time consuming; therefore, quick screening methods, especially when there is a need for quick diagnosis, is of great interest. Diagnostic methods which exploit subtle differences in breath sounds recorded during wakefulness, such as our group's Awake-OSA technology, have shown their capability to diagnose OSA at the research stage. Simplifying the breath sound recording procedure employed in the Awake-OSA diagnostic method would increase its efficiency when used in a clinical setting. In this study, we adopted breath sound data collected during wakefulness in two positions (sitting upright and supine) and two breathing maneuvers (nose and mouth breathing) from our previous study, and ran hypothesis tests on a wide variety of sound features to select the most significant features correlated with OSA. The goal was to investigate which combinations of patient position and breathing maneuver contribute the least to the significant features amongst groups of people with differing OSA severity, thus permitting simplification of the recording protocol. The results show that all signals recorded by a combination of the two breathing maneuvers and two positions result in features significantly correlated with OSA severity; this makes it impossible to confidently recommend that a combination be omitted from the recording protocol. Nevertheless, the results show that the majority of significant features originated from recordings made in the supine position. Therefore, as a step toward simplification of the Awake-OSA diagnostic algorithm, we may use breath sound signals recorded only in the supine position and further investigate the accuracy of the algorithm in distinguishing amongst groups with differing OSA severity.
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".