Arousal Responses during Overnight Polysomnography and their Reproducibility in Healthy Young Adults
Bibliographic record
Abstract
STUDY OBJECTIVES: Arousal intensity and heart rate (HR) response to arousal during polysomnography (PSG) vary considerably between patients with sleep disorders. Our objective was to determine the range of these arousal characteristics in healthy young adults and whether they are consistent on repeated testing. DESIGN: Post hoc analysis of 56 preexisting PSG files recorded from 28 healthy adults on 2 consecutive nights. SETTING: Academic medical center and Research and Development Laboratory (YRT Limited, Winnipeg, Manitoba, Canada). PARTICIPANTS: Twenty-eight healthy young adults. INTERVENTIONS: None. MEASUREMENTS AND RESULTS: Arousals were identified using an automatic system followed by manual editing. The intensity of arousals was scaled (between 0 and 9) using an automatic algorithm based on the change in the electroencephalography (EEG) signals' wavelet characteristics. 4,751 arousals in 28 pairs of PSGs (night 1 and night 2) were scaled. HR responses (ΔHR) to all arousals were determined and averaged at each arousal scale per file. Overall average arousal intensity ranged 3.0-7.1 in different subjects, and average ΔHR ranged 1.9-18.3 beats.min(-1). Heart rate response at a given arousal intensity, expressed as ΔHR at a moderate arousal scale of 5.0 (ΔHR5), ranged 4.1-18.1 beats.min(-1). There was a strong correlation between arousal intensity and ΔHR within each subject. More importantly, there were excellent intraclass correlations (ICC) between night 1 and night 2 results for all three variables (ICC = 0.72 for average intensity, 0.92 for average ΔHR4, and 0.91 for ΔHR5). CONCLUSIONS: Average arousal intensity and heart rate response to arousal are highly variable among healthy young adults and stable within individuals.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".