Physiological signal entropy in patients with chronic respiratory disease: a systematic review
Bibliographic record
Abstract
Background Chronic respiratory diseases (CRDs) such as COPD and asthma have a substantial impact on patients and healthcare systems. Recent research on diagnosing and monitoring CRDs highlights the potential of continuous measurement of physiological parameters using nonlinear measures such as entropy analysis. Entropy measures the irregularity and complexity of physiological signals, reflecting the engagement of physiological control mechanisms. This systematic review examines the current evidence on changes in the entropy of physiological signals in CRDs. Methods The review follows Preferred Reporting in Systematic Reviews and Meta-Analyses (PRISMA) guidelines and includes studies from databases such as Scopus, Medline, CINAHL and Embase. Quality assessment was conducted using the Newcastle–Ottawa Scale. Evidence was qualitatively synthesised, taking into account entropy signals, entropy type and results. Results 11 studies met the inclusion criteria. Entropy in signals including heart rate variability (HRV), airflow, peripheral oxygen saturation (SpO2), inter-breath interval and tidal volume were evaluated. The findings indicated that patients with COPD and asthma exhibit lower entropy in HRV and airflow compared to healthy controls, with entropy decreasing as disease severity increases. Conversely,SpO2entropy values were increased during an exacerbation compared to stable COPD. Conclusion The review highlights the potential of entropy analysis of physiological signals for early detection of COPD exacerbations and for differentiating between various levels of disease severity in both COPD and asthma. Additionally, it identifies research gaps, particularly in relation to other CRDs such as bronchiectasis and interstitial lung diseases. Further research is needed to facilitate the development of this approach into a fully effective tool for clinical practice.
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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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".