Sleep Stage Detection from Actigraphy and Heart Rate Using an Attention-Based Model
Bibliographic record
Abstract
Sleep plays a crucial role in human well-being, while insufficient sleep affects cognitive function, decision-making, and overall health. Sleep assessment via polysomnography (PSG) is time-consuming, resource-intensive, and limited to in-laboratory sleep testing. To address the challenges of PSG, wearable sleep screening devices have been widely used, especially to detect wakefulness and sleep stages. This study proposes deep models for the detection of wakefulness versus different stages of sleep using heart rate and wrist actigraphy extracted from the multi-ethnic study of atherosclerosis (MESA) sleep dataset. First, two sets of features were extracted from heart rate and actigraphy, which were separately fed into two separate branches of convolution neural network (CNN), then merged and fed to a deep classifier. The model detected wakefulness versus sleep and different sleep stages with the accuracies of 88.19% and 79.6% respectively. This work showed that combining heart rate, actigraphy signals, and demographic data in a deep framework could improve sleep stage-staging performance. This study offers a subject-specific approach for sleep assessment based on convenient wearables.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".