Characteristics and reproducibility of novel sleep EEG biomarkers and their variation with sleep apnea and insomnia in a large community-based cohort
Bibliographic record
Abstract
STUDY OBJECTIVES: New electroencephalogram (EEG) features became available for use in polysomnography and have shown promise in early studies. They include a continuous index of sleep depth (odds-ratio-product: ORP), agreement between right and left sleep depth (R/L coefficient), dynamics of sleep recovery following arousals (ORP-9), general EEG amplification (EEG Power), alpha intrusion and arousal intensity. This study was undertaken to establish ranges and reproducibility of these features in subjects with different demographics and clinical status. METHODS: We utilized data from the two phases of the Sleep-Heart-Health-Study (SHHS1 and SHHS2). Polysomnograms of 5,804 subjects from SHHS1 were scored to determine the above features. Feature values were segregated according to clinical status of obstructive sleep apnea (OSA), insomnia, insomnia plus OSA, no clinical sleep disorder, and demographics (age, gender, and race). Results from SHHS visit2 were compared with SHHS1 results. RESULTS: All features varied widely among clinical groups and demographics. Relative to participants with no sleep disorder, wake ORP was higher in participants reporting insomnia symptoms and lower in those with OSA (p < 0.0001 for both), reflecting opposite changes in sleep pressure, while NREM ORP was higher in both insomnia and OSA (p<0.0001), reflecting lighter sleep in both groups. There were significant associations with age, gender, and race. EEG Power, and REM ORP were highly reproducible across the two studies (ICC > 0.75). CONCLUSIONS: The reported results serve as bases for interpreting studies that utilize novel sleep EEG biomarkers and identify characteristic EEG changes that vary with age, gender and may help distinguish insomnia from OSA.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".