Maximizing the Yield of Rapid Eye Movement Sleep in the Epilepsy Monitoring Unit
Bibliographic record
Abstract
PURPOSE: Rapid eye movement (REM) sleep can help localize the epileptogenic zone in multifocal epilepsy when interictal discharges appear diffuse. However, REM sleep is reputedly rare and easily overlooked in the Epilepsy Monitoring Unit (EMU). The aims of this study are to determine the characteristics of REM sleep in a typical EMU and whether using automated artifact recognition can meaningfully enhance REM sleep detection. METHODS: Artifact-based REM sleep detection was applied to 581 nights of EMU recording from 100 patients over 12 months. REM sleep had been manually detected at the time of recording. The index of suspicion for manual detection was raised after 6 months. Artifact-based detection was compared with manual detection, and the impact on localization was assessed. RESULTS: REM sleep occurred in 77% of EMU nights. Thirty-six patients achieved REM sleep nightly and 62 patients on at least one night. Mean admission was 5.83 days. Mean REM sleep duration was 5.92 minutes over 1.88 mean nightly bouts. Raising the level of suspicion increased manual detection rates from 22.6% to 40.5%. The artifact-based detection rate was 96% and provided additional localizing information in 10% of epilepsy patients. CONCLUSIONS: REM sleep is common in the EMU, but bouts are few and brief. Capturing these bouts to maximize the yield of REM sleep in the EMU is made possible by automated artifact recognition whose results could enhance localization of the epileptogenic zone.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".