Aeroacoustics of breath sounds in trachea and upper airway
Bibliographic record
Abstract
Tracheal breathing sounds (TBS) are widely used in assessing respiratory disorders such as obstructive sleep apnea but a mechanistic relationship between airway morphology and aero-acoustics remains undefined. Here we use a realistic upper airway model reconstructed from a human CT scan to investigate aerodynamic and acoustic effects of velopharyngeal constriction on TBS. A hybrid aero-acoustic modeling approach was employed, combining computational fluid dynamics (CFD) with acoustic finite element simulation. The model was validated against recorded TBS and showed strong agreement in both amplitude and resonant frequencies. Simulation of four graded degrees of velopharyngeal constriction demonstrated a significant influence of geometric narrowing on airflow dynamics. Specifically, the pressure drop across the velopharyngeal segment (ΔP velopharynx ) followed a power law relationship with the percent area change (ΔA velopharynx ) with an exponent of 4.93 (R 2 = 0.998). Similarly, the dimensionless pressure coefficient (C p ) exhibited a strong correlation with (ΔA velopharynx ), with a power law exponent of 1.47 (R 2 = 0.999). Wall shear stress (WSS) at the velopharyngeal area increased dramatically with constriction severity, increasing 15-fold from 0.8 Pa to 12 Pa in the most severe case. These aerodynamic changes were closely linked to acoustic responses, leading to upward shifts in resonant frequencies within the [1000–1700] Hz range as the velopharyngeal area increased. These findings indicate a strong relationship between airway geometry and acoustic response, thus suggesting that TBS could be a valuable tool for quantitative non-invasive assessment of the upper airway in healthy and obstructive sleep apnea populations.
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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 | 0.001 | 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".