Motion Artifact Data to Facilitate Bioelectric Signal Quality Analysis Research
Bibliographic record
Abstract
Bioelectric signal quality analysis is important to address the challenges associated with contaminants, including motion artifacts.However, the limited availability of motion artifact data poses a challenge in developing and evaluating new tools (e.g., biases due to signal reuse).This research expands motion artifact data through two approaches.First, we deployed a Motion Artifact Signal Generation Toolkit to synthesize motion artifacts using Autoregressive, Markov Chain, and Recurrent Neural Network models.We extend model validation to non-cyclical motion artifacts.Second, we estimate motion artifacts in longterm ECG recordings using a template subtraction method, creating a motion artifact database of 84 signals.Additionally, we explore time-series segmentation of motion artifacts, leveraging a clustering pipeline, including k-means clustering, to partition longterm recordings based on signal statistics and attributes.These contributions greatly expand the public availability of motion artifacts for biomedical signal quality analysis researchers to use worldwide.vi 5.2.7 Post-Processing ......
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".