ANPHY-Sleep: an Open Sleep Database from Healthy Adults Using High-Density Scalp Electroencephalogram
Bibliographic record
Abstract
Well-documented sleep datasets from healthy adults are important for sleep pattern analysis and comparison with a wide range of neuropsychiatric disorders. Currently, available sleep datasets from healthy adults are acquired using low-density arrays with a minimum of four electrodes in a typical sleep montage. The low spatial resolution is thus prohibitive for the analysis of the spatial structure of sleep. Here we introduce an open-access sleep dataset from 29 healthy adults (13 female, aged 32.17 ± 6.30 years) acquired at the Montreal Neurological Institute. The dataset includes overnight polysomnograms with high-density scalp electroencephalograms incorporating 83 electrodes, electrocardiogram, electromyogram, electrooculogram, and an average of electrode positions using manual co-registrations and sleep scoring annotations. Data characteristics and group-level analysis of sleep properties were assessed. The database can be accessed through ( https://doi.org/10.17605/OSF.IO/R26FH ). This is the first high-density electroencephalogram open sleep database from healthy adults, allowing researchers to investigate sleep physiology at high spatial resolution. We expect that this database will serve as a valuable resource for studying sleep physiology and for benchmarking sleep pathology.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".