S4Sleep: Elucidating the design space of deep-learning-based sleep stage classification models
Bibliographic record
Abstract
Machine-learning-based automatic sleep stage scoring is a promising approach to enhance the time-consuming manual annotation process of polysomnography recordings. Although numerous algorithms have been proposed for this purpose, systematic exploration of architectural design decisions remains limited. This study conducts a comprehensive investigation into these design choices within the broad category of encoder–predictor architectures. The methodology identifies robust architectures applicable to both time series and spectrogram input representations, both of which leverage structured state space models as integral components. Without further hyperparameter adjustments, the proposed models S4Sleep(spec) and S4Sleep(ts) consistently surpass all existing approaches on the most commonly used benchmark datasets: Sleep EDF, the Montreal Archive of Sleep Studies, and, most notably, the extensive Sleep Heart Health Study dataset. The architectural insights derived from this research, along with the refined methodology for architecture search demonstrated herein, are expected to not only advance future research in sleep staging but also be beneficial for other time series annotation tasks. • Developed systematic method for finding optimal time series annotation architectures. • Identified encoder–predictor models with S4 as best performing architecture. • Demonstrated strong performance using both raw time series and spectrogram inputs. • Validated S4’s effectiveness as signal encoder for both input representations. • Surpassed benchmarks on three major sleep datasets (Sleep EDF, MASS, SHHS).
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".