Sleep stage classification in children using photoplethysmogram pulse rate variability
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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) | 0.000 | 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 it