Uncertainty Measurements in Non-Contact Neonatal Heart Rate Monitoring
Bibliographic record
Abstract
Continuously monitoring patient vital signs in the neonatal intensive care unit (NICU) requires wired sensors that can irritate fragile skin, motivating the development of non-contact physiologic signal estimation approaches. Such estimators typically involve a pipeline of multiple data analysis stages, including pre-processing, region of interest detection and tracking, and physiologic parameter estimation. Uncertainty in the estimated physiologic signal is often quantified strictly from the signal quality indicator (SQI) generated by the final stage of the pipeline. This manuscript proposes a framework to account for SQIs generated by each stage in a physiologic signal estimation pipeline and using the fusion of all SQIs to arrive at a refined measure of uncertainty in the final estimated physiologic parameter. This framework is demonstrated for heart rate (HR) estimation of newborns admitted in the NICU, where the pipeline includes bed occupancy detection, face detection, face tracking, and physiologic signal estimation. Different SQIs are derived at each stage and a novel fusion of all SQI metrics is shown to produce a more effective estimate of uncertainty in the final estimated HR.
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.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".