Finding the Lung sound-Flow Relationship in Normal and Asthmatic Subjects
Bibliographic record
Abstract
To investigate the relationship between lung sound (LS) and flow, we studied LS signals from 5 healthy adults (group I), 10 healthy children (group II) and 7 asthmatic children (group III). The LS signals were recorded on right upper lung lobe at different flow rates varied from 0.4 to 3.0 L/s and the flow signals were measured at mouth. The LS and flow signals were parsed into segments of 1024 data points with 50% overlap between successive segments. The mean LS amplitude (mean AMP) and mean flow (flow) were calculated for each segment. The average power (Pave) of each segment was calculated from LS spectrum for different frequency bands between 20-600 Hz. Four different types of models, representing the relationship between mean AMP or Pave and flow, were investigated using different percentage of flow signal in each inspiratory phase. The model coefficients were derived from either linear regression analysis or polynomial curve fitting between the data and model variables. The correlation coefficients (r) between the experimental data and data estimated from the model coefficients were calculated for each subject in each model and averaged between the subjects. The results showed much stronger correlation between Pave and flow than mean AMP and flow for all groups. The best model to describe Pave relationship with flow was found to be power relationship in both healthy adults and children whereas a third-order polynomial curve best fitted the Pave and flow data in asthmatic group. The optimum frequency band to calculate Pave was found to be 150-450 Hz for healthy subjects and 300-600 Hz for asthmatic children. The diminution of heart sound (HS) from LS recordings showed no change in the selected model in all three groups. The results of this study suggest the difference in Pave- flow relationship in healthy and asthmatic subjects may be used as a diagnostic tool for asthma.
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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 | 0.001 | 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".