Development and validation of an algorithm for the study of sleep using a biometric shirt in young healthy adults
Bibliographic record
Abstract
Portable polysomnography is often too complex and encumbering for recording sleep at home. We recorded sleep using a biometric shirt (electrocardiogram sensors, respiratory inductance plethysmography bands and an accelerometer) in 21 healthy young adults recorded in a sleep laboratory for two consecutive nights, together with standard polysomnography. Polysomnographic recordings were scored using standard methods. An algorithm was developed to classify the biometric shirt recordings into rapid eye movement sleep, non-rapid eye movement sleep and wake. The algorithm was based on breathing rate and heart rate variability, body movement, and included a correction for sleep onset and offset. The overall mean percentage of agreement between the two sets of recordings was 77.4%; when non-rapid eye movement and rapid eye movement sleep epochs were grouped together, it increased to 90.8%. The overall kappa coefficient was 0.53. Five of the seven sleep variables were significantly correlated. The findings of this pilot study indicate that this simple portable system could be used to estimate the general sleep pattern of young healthy adults.
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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 | 0.003 | 0.010 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".