A Machine Learning Analysis of Physiological Monitoring Signals to Detect Small Airway Narrowing Due to Cold Air Exposure in Asthma
Bibliographic record
Abstract
Asthma is a chronic inflammatory disease of the small airways, affecting over 200 million people globally. Cold air exposure is a potential risk factor for asthma exacerbations. We hypothesized that monitoring physiological signals during exposure to cold would help to detect potential worsening in asthma and can be used to help persons with asthma adjust their daily routine. Non-smoker adults (18-80 years) with asthma were asked to sit in a cold room of 0°C temperature for 10 minutes. During this period, Electrocardiogram (ECG) and thoraco-abdominal motion/respiration belt signals were measured continuously. At 0 and 10 min, small airway narrowing was assessed with oscillometry to estimate respiratory system impedance. Based on changes in respiratory impedance from 0 to 10 min, participants were grouped into with or without airway narrowing. After signal processing, we extracted time and frequency domain features from ECG and respiration signals. To classify airway narrowing, different machine learning classifiers were fine-tuned and evaluated using a leave-one-subject-out cross-validation approach. A total of 23 individuals (11 females, age: 56.3 ± 10.9 years, BMI: 27.4 ± 5.7 kg/m$^{2}$) with asthma were enrolled in the study. Up to 42% and 58% windows of signals were from individuals with and without airway narrowing, respectively. The support vector machine classifier performed the best compared to other models with an accuracy of 85%, precision of 87%, recall of 76%, specificity of 91%, and F1 score of 81%. These results provided proof of concept that technologies with embedded respiratory and cardiac signal monitoring may be able to predict airway narrowing during exposure to cold air in individuals with asthma.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".