On-line detection of sleep-wake states and application to produce intermittent hypoxia only in sleep in rats
Bibliographic record
Abstract
Sleep-disordered breathing is associated with adverse clinical consequences such as daytime sleepiness and hypertension. The mechanisms behind these associations have been studied in animal models, especially rats, but intermittent stimuli such as hypoxia have been applied without reference to sleep-wake states. To determine mechanisms underlying the adverse physiological consequences of stimuli associated with sleep-disordered breathing requires criteria for detection of sleep-wake states on-line to trigger stimuli only in sleep. This study aimed to develop such a system for freely behaving rats. Twelve rats with implanted electroencephalogram and neck electromyogram electrodes were studied in the light and dark phases. Electroencephalogram frequencies in the high (20-30 Hz) and low (2-4 Hz) frequency bands distinguished non-rapid eye movement (REM) sleep, whereas neck electromyogram distinguished REM. Using these parameters in a simple algorithm led to detection accuracies of 94.5 +/- 1.0 (SE) % for wakefulness, 96.2 +/- 0.8% for non-REM sleep, and 92.3 +/- 1.6% for REM compared with blinded human judgment. The algorithm was then used to trigger hypoxic stimuli only in sleep. Because frequency and amplitude analysis is readily performed using a variety of commercial systems, incorporation of these parameters into such an algorithm will facilitate studies investigating mechanisms underlying the physiological consequences of sleep-related respiratory stimuli in a fashion that more effectively models clinical disorders.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".