Non-contact assessment of obstructive sleep apnea cardiovascular biomarkers using photoplethysmography imaging
Bibliographic record
Abstract
Obstructive sleep apnea (OSA) affects 20% of the adult population, and is associated with cardiovascular and cognitive morbidities. However, it is estimated that up to 80% of treatable OSA cases remain undiagnosed. Cur- rent methods for diagnosing OSA are expensive, labor-intensive, and involve uncomfortable wearable sensors. This study explored the feasibility of non-contact biophotonic assessment of OSA cardiovascular biomarkers via photoplethysmography imaging (PPGI). In particular, PPGI was used to monitor the hemodynamic response to obstructive respiratory events. Sleep apnea onset was simulated using Muller's maneuver in which breathing was obstructed by a respiratory clamp. A custom PPGI system, coded hemodynamic imaging (CHI), was positioned 1 m above the bed and illuminated the participant's head with 850 nm light, providing non-intrusive illumination for night-time monitoring. A video was recorded before, during and following an apnea event at 60 fps, yielding 17 ms temporal resolution. Per-pixel absorbance signals were extracted using a Beer-Lambert derived light transport model, and subsequently denoised. The extracted hemodynamic signal exhibited dynamic temporal modulation during and following the apnea event. In particular, the pulse wave amplitude (PWA) decreased during obstructed breathing, indicating vasoconstriction. Upon successful inhalation, the PWA gradually increased toward homeostasis following a temporal phase delay. This temporal vascular tone modulation provides insight into autonomic and vascular response, and may be used to assess sleep apnea using non-contact biophotonic imaging.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".