Artifact management methodologies for arterial blood pressure signals: A systematic scoping review of human and animal literature
Bibliographic record
Abstract
Arterial blood pressure (ABP) is a broadly measured vital signal used to monitor cardiovascular health through raw signal and derived metrics. Artifacts are a pervasive issue, resulting in a declined utility of the signal. The aim of this review was to examine the existing literature pertinent to artifact management strategies in ABP signals. A search of five databases was conducted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. The search question examined existing algorithms for artifact management in ABP signals. The initial search yielded 15,564 articles (update included 1967 additional). The review included 73 articles. Categories included: (1) artifact management in low-frequency signals, (2) artifact management in full-waveform signals, (3) identification of valid or fiducial points. There were several algorithms that achieved strong effectiveness results. However, there was poor algorithm generalizability and inter-comparability due to insufficient population size, diversity in recording methodologies, and external validation. This made it difficult to quantitatively elucidate a leading method. Real-time utility was also not mentioned, critical for clinical use. This work provides novel insights into the literature regarding artifact management in ABP, identifying shortcomings and ways in which a more generalizable solution can be identified.
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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 | 0.033 | 0.135 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.023 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".