Pattern Recognition in Intra-Breath Oscillometry Measurements
Bibliographic record
Abstract
Intra-breath oscillometry (IBOsc) offers a high-resolution, non-invasive assessment of respiratory mechanics by tracking within-breath variations in respiratory impedance. Unlike traditional oscillometric approaches, which provide averaged impedance values over the entire respiratory cycle, IBOsc captures dynamic, nonlinear changes by analyzing single-frequency excitation signals. This study introduces a novel machine learning-based framework for automated pattern recognition in IBOsc data. The proposed pipeline incorporates artifact-tolerant preprocessing, impedance loop generation, feature engineering, and classification. Using carefully curated datasets from healthy individuals and patients with chronic obstructive pulmonary disease (COPD), interstitial lung disease (ILD), and obesity hypoventilation syndrome (OHS), the proposed framework successfully identifies clinically relevant patterns such as tidal expiratory flow limitation (tEFL). In the binary classification task distinguishing tEFL in healthy versus COPD patients, the best-performing model achieved an F1-score of 0.98 and an overall accuracy of 98.5% on a held-out test set of 204 samples. In the more complex three-class scenario involving healthy, COPD, and ILD patients, the model sustained strong performance, reaching a macro-averaged accuracy of 86.3% across 156 test samples, with a class-wise accuracy of 96.2% for tEFL detection. Beyond binary classification, the method proved effective in identifying both the presence and the resolution of tEFL patterns, which is a key clinical indicator for tracking therapeutic outcomes. The methodology demonstrated robustness across varying conditions and measurement setups, highlighting the potential of automated IBOsc analysis for enhancing clinical diagnostics and phenotyping of respiratory diseases.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".