Interbeat RR Interval Time Series Analyzed with the Sample Entropy Methodology
Bibliographic record
Abstract
Computational entropies are methods of non-linear analysis that allows an estimate of the irregularity of a system.Different types of computational entropy were considered and tested in order to obtain one that would give an index of signal complexity taking into account the size of the analysed time series, the computational resources demanded by the method, and the accuracy of the calculation.An algorithm for the generation of fractal time-series with a certain value of the spectral exponent β was used for the characterization of the different entropy algorithms.We obtained a significant variation for most of the algorithms in terms of the series size, which could result counterproductive for the study of real signals of different lengths.The best method was sample entropy, which shows great independence of the series size.With this method, time series of heart interbeat RR intervals or tachograms of healthy subjects and patients with congestive heart failure were analysed.The calculation of sample entropy was carried out for 24hour tachograms and time subseries of 6-hours for sleepiness and wakefulness.The comparison between the two populations shows a significant difference that is accentuated when the patient is sleeping.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".