Sleep stage classification in children using photoplethysmogram pulse rate variability
Bibliographic record
Abstract
Human sleep is classified into Rapid Eye Movement (REM) and non-REM sleep. In non-REM sleep, the heart rate and respiratory rate decrease whereas during REM sleep, breathing and heart rate become more irregular. As such, identification of sleep stages by monitoring the autonomic regulation of heart rate is a promising approach. In this study we analysed the standard features of heart rate variability extracted from the pulse oximeter photoplethysmogram (PPG) to identify different sleep stages. The overnight PPG signals were recorded from 146 children with the Phone Oximeter™ in addition to overnight polysomnography. The recordings were divided into 1-min segments and labelled as wake, non-REM and REM based on the event log file of the polysomnography. For each segment, six standard time and frequency domain features of heart rate variability were estimated. Two support vector machine classifiers were separately trained to classify wake from sleep and non-REM from REM sleep. Wake and sleep were classified with an accuracy of 77% and REM and non-REM were classified with an accuracy of 80%.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".