Sleep apnea severity based on estimated tidal volume and snoring features from tracheal signals
Bibliographic record
Abstract
Summary Sleep apnea can be characterized by reductions in the respiratory tidal volume. Previous studies showed that the tidal volume can be estimated from tracheal sounds and movements called tracheal signals. Additionally, tracheal sounds include the sounds of snoring, a common symptom of obstructive sleep apnea. This study investigates the feasibility of estimating the severity of sleep apnea, as quantified by the apnea/hypopnea index (AHI), using the estimated tidal volume and snoring sounds extracted from tracheal signals. Tracheal signals were recorded simultaneously with polysomnography (PSG). The tidal volume was estimated from tracheal signals. The reductions in the tidal volume were detected as potential respiratory events. Additionally, features related to snoring sounds, which quantified variability, temporal clusters, and dominant frequency of snores, were extracted. A step‐wise regression model and a greedy search algorithm were used sequentially to select the optimal set of features to estimate the apnea/hypopnea index and classify participants into healthy individuals and patients with sleep apnea. Sixty‐one participants with suspected sleep apnea (age: 51 ± 16, body mass index: 29.5 ± 6.4 kg/m2, apnea/hypopnea index: 20.2 ± 21.2 event/h) who were referred for a sleep test were recruited. The estimated apnea/hypopnea index was strongly correlated with the polysomnography‐based apnea/hypopnea index (R2 = 0.76, p < 0.001). The accuracy of detecting sleep apnea for the apnea/hypopnea index cutoff of 15 events/h was 78.69% and 83.61% with and without using snore‐related features. These findings suggest that acoustic estimation of airflow and snore‐related features can provide a convenient and reliable method for screening of sleep apnea.
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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 | 0.002 |
| 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".