<p>Autonomic Arousals as Surrogates for Cortical Arousals Caused by Respiratory Events: A Methodological Optimization Study in the Diagnosis of Sleep Breathing Disorders</p>
Bibliographic record
Abstract
INTRODUCTION: Portable monitoring (PM) is an alternative to laboratory polysomnography (PSG) for diagnosing obstructive sleep apnea (OSA). However, PM tends to underestimate the apnea-hypopnea index (AHI), as it does not identify non-desaturating events associated with electroencephalographic (EEG) arousal. The objectives were to explore heart rate acceleration (HRa) and decrease in pulse transit time (PTT) as surrogates to EEG arousal for non-desaturating hypopnea and respiratory effort-related arousal (RERA), and to estimate cut-off values for their use with both total sleep time (TST), the standard method for PSG, and total recording time (TRT), the usual method for PM. METHODS: Twenty-four consecutive individuals with suspected OSA were studied with PSG. Calculated outcomes were: AHI, respiratory disturbance index with EEG arousal (RDIe) and autonomic arousal by HRa (RDI-HRa) and PTT decreases (RDI-PTT) at different time cut-offs. Using RDIe as reference, Bland Altman and intraclass coefficient of correlation (ICC) were used to calculate agreement between indexes, and receiver operating curves (ROC) for sensitivity/specificity of the different cut-offs. RESULTS: Autonomic arousals, limited to respiratory events, were present in 36% of non-desaturating hypopneas and 29% of RERAs. Using TST, RDI-HRa of 10 bpm (ICC= 0.89) and RDI-PTT with a decrease of -15 msec (ICC=0.90) agreed better with RDIe. With TRT, the RDI-HRa of 5 bpm agreed better with the RDIe (ICC=0.89). Bland-Altman plots showed mean differences of 1.53 between RDI-HRa10-TST and RDIe and 0.89 between RDI-HRa5-TRT and RDIe. CONCLUSION: Autonomic arousals (HRa and PTT) may be a suitable proxy of EEG arousals associated with respiratory events, using both TST and TRT. Therefore, they could potentially help to capture borderline symptomatic patients and to monitor treatment outcomes.
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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 | 0.070 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".