The design of an audio recorder for respiratory sound recording
Bibliographic record
Abstract
Respiratory sound analysis offers critical and clear advantages for diagnosis and monitoring of respiratory disorders. Yet, the recording technology available today remains relatively undeveloped and non-specialized. Standard high-quality audio recording systems often do not capture the low-frequency spectrum of respiratory sounds; not to mention such equipment typically has high associated costs. Contributing to the issue is a lack of standardization of equipment used; hence, what follows is a large variability of the recordings and the inability to effectively compare results between different recording systems. The objective of the following presented work was to design and build an electronic audio recording device along with microphone and suitable air-chamber to be placed over the trachea or lung for capturing respiratory breathing sounds. Design objectives included maintaining a cost-effective, portable, and small form-factor for the device as well as compatibility with our team’s patented obstructive sleep apnea (OSA) diagnostic algorithms to detect for the severity of OSA during either overnight sleep or wakefulness. All desired objectives for the design were able to be realized in a compact, cost-effective, and highly accurate device. When reviewing the tracheal breathing sound recordings conducted with the device, the results identify signal content, albeit low amplitude, past 5 kHz up to 9 kHz; that indicates the previous cut-off point of many precursory studies might not have been adequate for capturing the entire characteristic features of tracheal respiratory sounds.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".