Detection of Respiratory Sounds at the External Ear
Bibliographic record
Abstract
Several clinical and ambulatory settings necessitate respiratory monitoring without a mouthpiece or facemask. Several studies have demonstrated the utility of breathing sound measurements performed on the chest or neck to detect airflow. However, there are limitations to skin surface measurements, including susceptibility to external noise and transducer motion. Thus, this two-part study investigated a novel location for breathing sound measurements: the external ear. The first study investigated characteristics of sound transmission from the oropharynx to the external ear in 19 adults (nine males). Broadband noise was directed into the oropharynx through a tube and mouthpiece and measured indirectly via an accelerometer affixed to the cheek. Resultant transmission to the external ear was measured with a microphone inserted into an earplug that provided acoustic isolation from ambient noise. Near-unity coherence estimates (> 0.9) between the sounds recorded at the external ear and the oropharynx were observed up to approximately 800 Hz, indicating a low-frequency region of preferred transmission. In the second study, each of 20 subjects (nine males) breathed through a pneumotachograph at targeted shallow (3.0 mL/s/kg) and tidal (7.5 mL/s/kg) flows normalized to body mass, and the resulting sounds were recorded at the external ear. Recordings during breath hold measured background noise. Shallow and tidal expiratory flows, respectively, produced signal-plus-noise-to-noise [(S + N)/N] ratios of 6.7 +/- 4.1 dB and 14.0 +/- 5.3 dB (mean +/- standard deviation) across all subjects between 150 and 300 Hz. Concurrent inspiration demonstrated (S + N)/N ratios of 6.6 +/- 3.9 dB and 14.9 +/- 6.3 dB. Thus, the external ear shows promise as an anatomic site to detect and monitor breathing in a relatively noninvasive and unobtrusive manner.
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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) | 0.002 | 0.001 |
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".