Revealing the Dynamic Correlation betweenCardiac and Respiratory HemodynamicSignals Using Time-Dependent Panel Co-Integration Analysis
Bibliographic record
Abstract
Panel Co-integration is one of the most popular methods to reveal and evaluate the influence and the interactions that may exist between long-term variables; Co-integration is used in different field and enjoying unprecedented popularity due reliability, clarity and robustness. In this article, we conducted an investigation using very powerful mathematical model to verify if a long-term relationship exists between Cardiorespiratory hemodynamic signals, the methodology we applied in this paper is mainly based on four basic parts, in first part we applied unit root tests to assess the stationarity of series, the second step is to apply the test of Co-integration model and quantify this long-term relationship. Finally we applied the Granger causality tests to the entire panel that consists of 186 patients taken from Montreal Hospital / General MF (Massachusetts General Hospital / Marquette Foundation) database. The results found in this study show the long-term interaction between the Cardiorespiratory hemodynamic signals, and reveals how the understanding of these interactions can help the doctors to understand the risks that may exist between these interactions. The originality of this article is the number of variables incorporated in our model study. Unlike the majority of studies that are conducted with only two variables, our study is specified by its multidimensionality. The main advantage of a multidimensional and multivariate model is to solve a multitude of problemsthatprevent doctors to treat the patients betterandis not the case for studies in two dimensions.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".