A flexible approach for simulating physiological signals
Bibliographic record
Abstract
Generating synthetic physiological signals using information extracted from real world physiological signals plays an important role in the field of medical device development and education. Most of the existing approaches are limited in the sense that they either focus on a particular physiological signal or lack flexibility in generating signals that mimic real world scenarios. In this paper, we present a cubic B-Spline interpolator-based flexible signal generator intended for simulating a variety of physiological signals. A simulated artifact generator (SAG) is also included in the proposed scheme to add artifacts to the physiological signals mimicking signal deviations associated with real world scenarios. In addition, the proposed method offers the ability to easily present a parametric representation to model a case-specific physiological signal. To demonstrate the ability of the proposed method, case studies on electromyogram (EMG), electro-oculogram (EOG), and electrocardiogram (ECG) during ventricular fibrillation are presented. Using a database of 20 ECG signals, the proposed approach was compared with an existing-model-based method and the results confirm the flexibility of our proposed approach as well as higher signal reproduction accuracy (a mean root mean square error improvement of 47.9% for waveform-based modeling and 4.3% for parametric-based modeling).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".