SLEEPYLAND: trust begins with fair evaluation of automatic sleep staging models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Automatic sleep staging with deep learning has advanced considerably, yet clinical adoption remains hindered by limited generalization, model bias, and inconsistent evaluation practices. We present SLEEPYLAND, an open-source framework comprising ~ 220,000 h of in-domain and ~ 84,000 h of out-of-domain polysomnographic recordings, spanning diverse ages, disorders, and hardware configurations. We release pre-trained state-of-the-art models, evaluating them across single- and multi-channel EEG/EOG setups. We introduce SOMNUS, an ensemble that integrates models via soft-voting, achieving robust performance across 24 datasets (macro-F1, 68.7-87.2%), outperforming individual models in 94.9% of cases and exceeding prior state-of-the-art. Exploiting the Bern-Sleep-Wake-Registry (N = 6633), we show that while SOMNUS improves generalization, no model architecture consistently minimizes model demographic/clinical bias. On multi-annotated datasets, SOMNUS surpasses the best human scorer (macro-F1, 85.2% vs 80.8% on DOD-H, and 80.2% vs 75.9% on DOD-O), more closely reproducing consensus. Finally, ensemble disagreement metrics predict scorer ambiguity (ROC-AUC 82.8%), providing reliable proxies for human uncertainty.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.000 | 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 it