0069 DEVELOPMENT AND VALIDATION OF AN ALGORITHM FOR THE STUDY OF SLEEP USING A BIOMETRIC SHIRT IN YOUNG HEALTHY ADULTS
Bibliographic record
Abstract
Portable polysomnography systems are often too complex and encumbering for home sleep recordings. We assessed the feasibility of measuring sleep with a biometric shirt. Twenty healthy young adults (12 women, 8 men; 21.9 ± 2.0 years) were recorded in a sleep laboratory for two consecutive nights using standard polysomnography and a biometric shirt, simultaneously. Polysomnographic recordings were scored using standard methods. The biometric shirt had embedded electrocardiogram sensors, two respiratory inductance plethysmography bands, a 3-axis accelerometer and a detachable microcontroller performing signal acquisition, data processing and communication protocols. The shirt size was selected for each subject so that the signal was optimal. An algorithm was developed to classify the biometric shirt recordings into three vigilance states: wake, nonREM sleep and REM sleep. The algorithm was based on breathing rate and heart rate variability, body movement and included a correction for sleep onset and offset. The results from the two types of recordings were compared with percentages of agreement and kappa coefficients. Five nights from four subjects were rejected due to recurrent signal artefacts caused by an ill-fitting or misplaced shirt. The overall mean percentage of agreement for 35 recording pairs was 77.55%. When NREM and REM sleep epochs were grouped together, the agreement was 90.7%. The overall kappa was 0.53. Removing breathing rate from the algorithm decreased kappa to 0.34 ± 0.13, whereas removing heart rate did not significantly modify it (0.54 ± 0.13). Five of the seven sleep variables were significantly correlated (sleep latency, total sleep time, %NREM and %REM sleep, the sleep period, wake time after sleep onset and sleep efficiency) while the minutes spent in NREM and of REM sleep did not. The findings of this pilot study indicate that a simple portable system using a biometric shirt can estimate reasonably well the general sleep pattern of young healthy adults. Fondation Les Petits Trésors de l’Hôpital Rivière-des-Prairies, Montréal, QC Canada.
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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 | 0.004 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".