Noise Removal of Tracheal Sound Recorded During CPET to Determine Respiratory Rate
Bibliographic record
Abstract
This study aimed to extract respiratory signal from tracheal sound recordings during cardio-pulmonary exercise test (CPET), which is the state-of-the-art diagnosis of cardiovascular and respiratory diseases. However, the availability of CPET is limited due to its expense. Physiological signal analysis is a convenient alternative to measure clinical parameters. We collected data from 30 healthy adults and applied wavelet transform thresholding (WTT), empirical mode decomposition (EMD), and Synchrosqueezing transform filtering (SST) to de-noise the raw data. Signal to noise ratio (SNR) was calculated as a quantitative measure of signal quality. We observed that SST yielded the highest SNR and introduced lowest signal distortion by visual-auditory inspection. Respiratory rate was then determined using the signal extracted by SST. Discrepancy of respiratory rate determined from the signal and the gold standard CPET was within 1.2 breaths per minute. In conclusion, SST effectively removed noises in tracheal sound recorded during CPET and provided analyzable respiratory signal for clinical parameter determination.
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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 | 0.002 |
| 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".