Effects of exercise and passive head-up tilt on fractal and complexity properties of heart rate dynamics
Bibliographic record
Abstract
tk;1Passive head-up tilt and exercise result in specific changes in the spectral characteristics of heart rate (HR) variability as a result of reduced vagal and enhanced sympathetic outflow. Recently analytic methods based on nonlinear system theory have been developed to characterize the nonlinear features in HR dynamics. This study was designed to assess the changes in the fractal and complexity measures of HR behavior during the passive head-up tilt and during exercise. Fractal exponent (alpha(1)) and approximate entropy (ApEn), measures of short-term correlation properties and overall complexity of HR, respectively, along with spectral components of HR variability were analyzed during a passive head-up tilt test (n = 10) and a low-intensity steady-state exercise (n = 20) in healthy subjects. We observed that alpha(1) increased during the tilt test (from 0.85 +/- 0.22 to 1.48 +/- 0.20; P < 0.001) and during the exercise (from 1.00 +/- 0.22 to 1.37 +/- 0. 14; P < 0.001). ApEn also increased during the exercise (from 1.04 +/- 0.11 to 1. 11 +/- 0.08; P < 0.05), but it did not change during the tilt test. The normalized high-frequency spectral component decreased and the low-frequency component increased similarly during both the exercise and the tilt test (P < 0.001 for all). Exercise and passive tilt result in an increase of short-term fractal correlation properties of HR dynamics, which is related to changes in the balance between the low- and high-frequency oscillations in controlled situations. Overall complexity of HR dynamics increases during exercise but not during passive tilt.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".