Multifractal characterization of synthetic ECG in the presence of coloured noise
Bibliographic record
Abstract
The electrocardiogram (ECG) is a signal resulting from a complicated dynamical process. The paper describes an extension to a previous method of reconstruction and characterization of real ECG to a new "realistic" dynamical model of an ECG. A noise-free ECG is generated at 360 samples per second from the dynamical model, and contaminated with 10 dB of white, pink, or brown noise. The dynamical phase-space is then reconstructed. It is observed that the noise-free ECG has a Renyi fractal dimension spectrum (RS) bounded between 1 and 2. It is also observed that noise introduces a positive bias in the RS estimate. This bias is greater for uncorrelated noise, but more asymmetric with increasing correlation. These are the first characterizations of a realistic estimate of the noise-free ECG attractor. It is also the first analysis of the effect of noise correlation on the RS of ECGs in isolation of sampling effects. This demonstrates that a disagreement with the RS of a real ECG signal is not due to noise.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".