A New Approach for Automatic Sleep Staging: Siamese Neural Networks
Bibliographic record
Abstract
Sleep staging aims to gather biological signals during sleep, and categorize them by sleep stages: waking (W), non-REM-1 (N1), non-REM-2 (N2), non-REM-3 (N3), and REM (R). These stages are distributed irregularly, and their number varies with sleep quality. These features adversely affect the performance of automatic sleep staging systems. This paper adopts Siamese neural networks (SNNs) to solve the problem. During the network design, seven distance measurement methods, namely, Euclidean, Manhattan, Jaccard, Cosine, Canberra, Bray-Curtis, and Kullback Leibler divergence (KLD), were compared, revealing that Bray-Curtis (83.52%) and Cosine (84.94%) methods boast the best classification performance. The results of our approach are promising compared to traditional methods.
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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 | 0.000 | 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) | 0.001 | 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 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".