A Bayesian Multi-Scale Framework for Photoplethysmogram Imaging Waveform Processing
Bibliographic record
Abstract
Photoplethysmography imaging (PPGI) is an increasingly populartechnique for remotely creating signals with a plethora of medicalinformation, referred to as PPGI waveforms. However, PPGI waveformsare often heavily affected by illumination variation and motionartefacts. Current PPGI waveform processing methods are usefulfor estimating heart rate, however, structural detail is not preserved,rendering the signal incapable of providing additional medical information.For this reason, we propose a multi-scale framework basedon the Bayesian residual transform which aims to suppress noiseand preserve structural details necessary for extracting cardiovascularinformation beyond the scope of heart rate. Experiments conductedon a dataset consisting of 24 different PPGI waveforms andcorresponding PPG waveforms captured via a finger pulse oximetersuggests a high level of noise and ambient illumination variationsuppression is achieved while signal fidelity is largely retained.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".