Using tracheal breathing sounds and anthropometric information for screening obstructive sleep apnoea during wakefulness
Bibliographic record
Abstract
Obstructive sleep apnoea (OSA) is a common yet underdiagnosed disorder. Undiagnosed OSA significantly increases perioperative morbidity and mortality for OSA patients undergoing surgery, requiring full anaesthesia. Tracheal breathing sounds characteristics during wakefulness have shown a high correlation with the apnoea-hypopnea index (AHI), while they are also affected by the anthropometric parameters, e.g., sex, age, etc. This study investigates the effects of the anthropometric parameters on our new quick objective OSA screening tool during wakefulness. Breathing sounds of 122 individuals (71 with AHI <15 as non-OSA and 51 with AHI > 15 as OSA) were recorded during wakefulness in the supine position. The spectra and bi-spectra of 81 (47 non-OSA) individuals' signals, which were randomly selected, were analysed as a training dataset to extract the most significant features with the lowest sensitivity to the anthropometric parameters. Using a support vector machine (SVM) classifier, these features resulted in 72.1, 64.7 and 77.5% testing classification accuracy, sensitivity and specificity, respectively. We also investigated classifying subjects into subgroups related to each anthropometric parameter and incorporating a voting procedure. This routine resulted in 83.6, 74.5 and 90.1% testing classification accuracy, sensitivity and specificity, respectively. In conclusion, it is possible to positively utilise the anthropometric information to enhance the classification accuracy for a reliable OSA screening procedure 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) | 0.000 | 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) | 0.001 | 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".