Inline inspiratory infrasound detection during bilevel mask ventilation
Bibliographic record
Abstract
Introduction: Infrasound (<20 Hz) is generated by many natural and biological phenomena. A stethoscope has been developed to record infrasound, however, such auscultation would be subject to ambient and body surface noise along with interposed tissue dampening. Aim: To record respiratory infrasound from oronasal breath sounds inside a mask before and during bilevel positive airway pressure (BiPAP) ventilation. Methods: A wireless 16 bit accelerometer was mounted securely inside an oronasal mask. 30 s audio epochs were sampled from DC at 1600 Hz and transmitted via Bluetooth (BLE) to a host computer running Ubuntu 22.04 Linux using a custom Python3 script in healthy adult subjects. Ambient unattached and applied mask with and without BiPAP (20/16 cmH2O) .wav files were plotted as spectrograms using Audacity 2.4.2. audio software. Results: Nonambient inspiratory 4 to 5, 9 to 10, and 20 Hz infrasound peaks as fundamental, first overtone, and second overtone frequencies were evident. These peaks were not substantially affected by differences in respiratory rate or by BiPAP. Conclusion: Digital accelerometry can effectively identify oronasal inspiratory infrasound.
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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 | 0.000 |
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".