Nonstationary analysis of cerebral hemodynamics using recursively estimated multiple-input nonlinear models
Bibliographic record
Abstract
We present a computational scheme to obtain adaptive non-linear, multiple-input models of the Volterra-Wiener class, by utilizing Laguerre expansions of Volterra kernels in a recursive least-squares formulation. Function expansions have been proven successful in systems identification as they result in a significant reduction of the required free parameters, which is a major limiting factor particularly for nonlinear systems, whereby this number depends exponentially on the nonlinear system order. We apply this scheme in order to obtain adaptive estimates for a two-input model of cerebral hemodynamics, where the two inputs are arterial blood pressure (ABP) and end-tidal CO2(PETCO2) variations and the output is cerebral blood flow velocity (CBFV) variations, by utilizing long-duration (40 min) experimental measurements of spontaneous variations of these signals in healthy humans. Maintenance of a relatively steady cerebral blood flow, despite changes in arterial pressure, is critical in order to meet the high metabolic demands of the brain. This is achieved by the synergistic action of various physiological factors, which may vary over different time-scales and also exhibit nonstationarities. We quantify these nonstationarities for the two main physiological determinants of cerebral blood flow variability (i.e., arterial pressure and arterial CO2) by considering one- (ABP) and two-input (ABP and PETCO2) models. The results illustrate the presence of nonstationarities which are frequency-dependent and also that incorporation of PETCO2as an additional input, results in estimates of dynamic pressure autoregulation that are more consistent with respect to time.
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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 | 0.001 |
| 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 | 0.001 |
| 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".