Intra-breath measurements - supporting information of “Pattern recognition in intra-breath oscillometry measurements"
Bibliographic record
Abstract
This measurement dataset is part of the supporting information for our article entitled “Pattern recognition in intra-breath oscillometry measurements”, which can be found through the references provided in the publication.In addition to presenting the detailed methodology and results of our analysis, the article serves as a general guide for conducting machine learning (ML)-based classification using intra-breath oscillometry (IBOsc) data. It includes guidelines on signal preprocessing, feature extraction, artefact detection, aggregation strategies, and interpretable ML workflows.About Intra-Breath Oscillometry (IBOsc):The article introduces intra-breath oscillometry in more detail. Briefly, oscillometry is a non-invasive method for evaluating the mechanical properties of the respiratory system. The novel intra-breath variant (IBOsc) employs a single-frequency test signal (typically 10 Hz in adults) and computes respiratory impedance over short, overlapping time windows (approximately 0.1 seconds), capturing dynamic changes throughout the breathing cycle.Dataset Structure:The dataset includes one measurement visit for each of 871 patients.Each patient has a dedicated folder, which may contain multiple measurement files, reflecting repeated or segmented recordings from the same visit.All measurements were acquired using the tremoflo® C-100 device (THORASYS Thoracic Medical Systems Inc., Montreal, QC, Canada) in its research mode with 10 Hz single-frequency excitation.Subjects were seated, used a nose clip, and supported their cheeks with their hands, following the technical recommendations for routine oscillometry.Recordings lasted 20–30 seconds, capturing several full breathing cycles.The pressure and flow signals were sampled at 256 Hz and exported using the manufacturer’s software.labels.csv file contains metadata for the dataset. This file includes an index of the patients, the number of measurement files available for each patient, and their corresponding diagnostic label: healthy, COPD, or ILD.Data Content:Each column in the .txt.inpx data files is explained in the article:Time (second): Timestamps for each sample.Vol (liter): Volume calculated from the flow signal.Pcyl (cmH₂O): Measured pressure.Flow (L/s): Respiratory flow signal.The paper provides step-by-step instructions on how to process these signals to compute the mechanical impedance of the respiratory system (Zrs) and offers explanations for all signal components involved.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.154 | 0.086 |
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".