A Toolkit for Motion Artifact Signal Generation
Bibliographic record
Abstract
In the research and development stages of biomedical signal quality analysis tools, testing and validation help to ensure they work as intended and are robust enough to be used in all sorts of environments. Large datasets of biomedical signals (e.g., electrocardiogram, electromyogram) and signal contaminants (e.g., motion artifact, power line interference) are required for rigorous testing; however, obtaining a large, diverse database of real-life signals and contaminants is a challenging process. By accurately simulating signals and contaminants, researchers are able to more easily create large amounts of data, with known levels of contamination, which can be used for testing and validation of signal quality analysis tools. The Motion Artifact Signal Generation Toolkit allows for the synthesis of motion artifacts using one of three models: 1) autoregressive, 2) Markov chain, and 3) recurrent neural network. Each of these has been prepared for three use-cases: 1) pre-simulated motion artifacts, 2) pre-trained models that can be used to simulate motion artifacts, and 3) training a model using a motion artifact sample and using that model to simulate motion artifacts. The three model types were tested on nonstationary data, exposing some current limitations; specifically, the models' ability to model real-world, non-cyclical data. The recurrent neural network does appears to produce reasonable simulated motion artifact that exhibit similarities, in both the time and frequency domains, to short time segments of real-world motion artifact.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.049 | 0.026 |
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".