Open‐loop Analysis of Transfer Characteristics from Blood Pressure to Heart Rate Using an Effectively Total Artificial Heart
Bibliographic record
Abstract
It is desirable for the dynamic behavior of the drive rate of the artificial heart to be as similar as possible to that of the recipient's heart rate (HR) before implantation. This requires a model which can simulate the behavior of HR on the basis of only the information measured with the limited number of approvable implanted sensors. This article provides a linear time series model for explaining the behavior of HR only with aortic pressure and right atrial pressure. This could be obtained from open-loop analysis using a total artificial heart, which was introduced for measuring HR in vivo and for eliminating its effect on blood pressure. The model was identified in a goat equipped with a special biventricular assist device called the effectively total artificial heart (ETAH). The ETAH was introduced to make an open loop and awake situation in the animal with almost intact autonomic nerves, which could enhance the accuracy and reliability of the identification of the model. The adequacy of the proposed model was ascertained in several data sets measured in two goats, which were different from the data set used for identification. Most of the mean estimation errors were less than 3 beats/min and auto-correlation analysis showed approvable statistical appropriateness. However, it was clarified through comparison with the 1/R control method that the proposed model has a few problems still to be solved before its future implementation as an automatic controller of the TAH.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".