Evaluation of the Signal Quality of Wrist-Based Photoplethysmography
Bibliographic record
Abstract
Detection of paroxysmal atrial fibrillation requires continuous cardiovascular monitoring due to its episodic nature.Such monitoring is impractical with electrocardiogram Holter monitors, which are the currently employed for ambulatory cardiovascular monitoring, but are cumbersome for prolonged use.This thesis studies monitoring using photoplethysmography (PPG) devices, which may be embedded into wristband devices which can be easily worn continuously.However, the quality of wrist-based PPG is highly variable, and is subject to artifacts from motion and other interferences.The goal of this thesis is to evaluate the signal quality obtained from wrist-based PPG when used in an ambulatory setting.Ambulatory data is collected over a 24-hour period for 10 elderly, and 16 non-elderly participants.Visual assessment is used as the gold standard for PPG signal quality, with Fleiss's Kappa being used to evaluate the agreement between raters.With this gold standard, 5 classifiers are evaluated using a modified 13-fold cross-validation approach.Based on this evaluation, a Random Forest quality classification algorithm is selected, with an accuracy of 74.5%.The algorithm is used to evaluate the ambulatory use of wrist-based PPG over a 24-hour period.Overall, it is found that data quality is high at night, and low during the day.First and foremost, I would like to acknowledge all the support received from my family, without which none of this would have been possible.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".