Predicting Heart Rate Estimation Accuracy: Photoplethysmography Sensor Measurements on Eight Anatomical Sites <sup>*</sup>
Bibliographic record
Abstract
The widespread use of Photoplethysmography (PPG) wearables in various form factors has facilitated long-term continuous cardiac monitoring beyond clinical settings. This study aims to investigate the accuracy of infrared PPG sensors in heart rate (HR) estimation across eight anatomical sites and to characterize the relationship between PPG waveform features and HR estimation accuracy at these sites. We collected PPG data from eight anatomical locations (fingertip, finger base, wrist, forearm, earlobe, mastoid bone, temple, and forehead) to estimate HR. Electrocardiogram (ECG) data were collected and used as ground truth for HR measurement. PPG-based HR error was defined as the normalized difference between PPG- and ECG-based HR values. We quantified the associations between PPG waveform characteristics and HR error using two signal quality metrics: perfusion index (PI) and cardiac power ratio (PR). We found that PI effectively predicted HR errors at anatomical sites with strong pulsatile components (e.g., fingers) but was less reliable at sites with weaker pulsatile characteristics (e.g., wrist). In contrast, PR provided more accurate HR error predictions at sites with weaker pulses. These findings suggest that PR could serve as a more reliable metric for predicting HR errors. Specifically, PR would allow the use of all recordings, enabling users to assess the reliability of reported HR values based on predicted HR error, rather than discarding "low-quality" recordings, as is commonly done with metrics such as PI.Clinical Relevance-In this study, we characterized the relationship between two PPG signal quality metrics (PI and PR) and PPG-based HR estimation errors. These metrics offer insights into the accuracy of HR measurements, enabling clinicians to better assess the reliability and trustworthiness of physiological characteristics across different anatomical sites.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".