Changes in physiological entropy signals in patient with obstructive sleep apnoea: a systematic review
Bibliographic record
Abstract
Introduction: Obstructive sleep apnoea (OSA) affecting an estimated 936 million people worldwide, yet only 15% receive a definitive diagnosis. OSA diagnosis poses challenges due to the dynamic nature of physiological signals such as oxygen saturation (SaO2) and heart rate variability (HRV). Linear analysis methods may not fully capture the irregularities present in these signals. The application of entropy offers a promising method to better measure irregularities in dynamic biological data. Aim and objective: To explore entropy changes in physiological signals among individuals with OSA. Methods: Keyword and title searches were performed on Medline, Embase, Scopus, and CINAHL databases. Studies had to analyse physiological signals in OSA using entropy. Quality assessment used the Newcastle–Ottawa Scale. Evidence was qualitatively synthesized, considering entropy signals, entropy type, and time-series length. Results: 23 studies were included. Multiple physiological signals related to OSA, including SaO2, HRV, and the oxygen desaturation index (ODI), have been investigated using entropy. Results revealed a significant decrease in HRV entropy in OSA compared to control groups. Conversely, SaO2 and ODI entropy values were increased in OSA. Despite variations in entropy types, time scales, and data extraction devices, studies using receiver operating characteristic (ROC) curves demonstrated a high discriminative accuracy (>80% AUC) in distinguishing OSA patients from control groups. Conclusions: This review highlights the potential of entropy analysis in developing new diagnostic indices for patients with OSA. Further investigation is needed before applying this technique clinically.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".