Assessing Obstructive Sleep Apnea Severity During Wakefulness via Tracheal Breathing Sound Analysis
Bibliographic record
Abstract
Obstructive sleep apnea (OSA) is a commonly underdiagnosed condition that not only increases the risk of accidents but also significantly contributes to a wide range of health complications, including heightened perioperative morbidity and mortality risks during surgeries under general anesthesia. Polysomnography (PSG), which is the diagnostic gold standard, is costly, requires skilled technicians, is time-consuming, and is not always accessible. This study presents a fast, objective, and non-invasive method for detecting OSA severity by analyzing tracheal breathing sounds (TBS) recorded during wakefulness in supine position. Features were extracted from six binary (1-vs-1) severity comparisons-Non-OSA, Mild, Moderate, and Severe-and combined with anthropometric characteristics for classification. The data of 199 subjects (74 Non-OSA, 35 Mild, 50 Moderate, and 40 Severe) were analyzed, the data of 169 and 30 was used for training and blind testing, respectively, and the training dataset was shuffled 10 times to avoid any bias during training. Multiple machine learning models were evaluated, and the best-performing model for each was saved. Across six experimental models comparing OSA severity levels, the most balanced performance was achieved by the Base Model of Non-OSA vs. Severe-OSA using the support vector machine algorithm, with 88.2% accuracy, 83.3% sensitivity, and 90.9% specificity. While Random Forests in the Base Model of Non-OSA vs. Mild-OSA achieved 100% sensitivity, its accuracy was lower (81.2%). The results confirm the reliability and robustness of the proposed approach, providing a basis for OSA severity screening in under 10 min during wakefulness.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".