Assessing Photoplethysmogram (PPG) Signal Quality Across Participant Groups and Settings
Bibliographic record
Abstract
Photoplethysmography (PPG) is commonly used in consumer wrist-worn devices for continuous cardiac monitoring. However, PPG signals are highly susceptible to artifacts, which can compromise measurement accuracy. To address this, various signal quality metrics have been proposed (e.g., pulse skewness, perfusion index). While effective for singlepulse analysis, these metrics are less suitable for continuous monitoring or regions with weaker pulsatile signals, such as the wrist. This study investigates the power ratio (PR), a frequency-domain metric, for segment-wise PPG signal quality assessment in wrist-worn devices. We collected PPG and accelerometer data from 46 participants representing both non-clinical and clinical populations. Low-motion PPG segments were identified, and a subset of these recordings was manually annotated for signal quality. Results demonstrate that PR effectively distinguishes between high- and low-quality segments, particularly when the signal's dominant frequency is disrupted or when abnormal amplitude patterns alter the spectral distribution. However, PR showed limited sensitivity to morphological distortions that do not significantly affect frequency content. A PR threshold derived from the non-clinical dataset generalized well to clinical populations, though individual variability (e.g., significantly elevated heart rate) challenged its robustness. These findings support the use of PR as a conservative segment-level quality metric for wrist-worn PPG monitoring. However, integrating PR with complementary quality metrics may further enhance its detection capabilities across a broader range of signal degradations and improve clinical utility.
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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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 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".