iSleep: Continuous, binocular pupil tracking in sleep and reduced consciousness for physiological monitoring, predictions and interventions
Bibliographic record
Abstract
Abstract Monitoring pupil dynamics is a key tool in understanding arousal. Pupil size can serve as a biomarker for the autonomic nervous system balance as well as for identifying brain states. While internal states can also be self-reported when awake, automated detection and non-invasive monitoring is crucial during sleep and reduced consciousness. Here, we introduce iSleep, an innovative pupil tracking and analysis framework for sleep in humans. It features comfortable, humidified eye-tracking goggles and a platform for integrated analysis and prediction capabilities. We show that iSleep allows safe and continuous access to binocular pupil size and ocular dynamics during sleep and anesthesia. iSleep reveals that pupillary fluctuations correlate tightly with brain activity, heartbeat, and breathing, and can reliably predict brain states. Pupil constrictions reflect parasympathetic drive and likely serve a protective function for deep sleep stability; while dilations indicate arousals. Unexpectedly, we observed a decoupling of binocular movements during periods of sleep, indicating alterations in reflexes which usually govern voluntary eye movements. Finally, iSleep was tested in surgery patients under general anesthesia, revealing dynamic pupil changes to noxious stimuli, suggesting the potential for nociception monitoring during surgeries. In summary, iSleep offers an easy-to-use, robust alternative to read out brain states during sleep and anesthesia, opening new avenues in monitoring, diagnostics, and treatments, previously obscured by closed eyelids.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".