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Updated: Jul 17, 2026

Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression
Published on: December 10, 2014
Kalman filter modeling of cerebral blood flow autoregulation
M A Masnadi-Shirazi1, K Behbehani, R Zhang
1Shiraz Univ., Iran.
Abstract:
A parameter estimation scheme for dynamic systems is employed to simultaneously estimate the states and parameters of the model of human cerebral blood flow velocity as a function of mean arterial blood pressure. The estimation results show 20-40% reduction in the output mean square error compared to that of the one obtained from the computer model addressed in [1]. The estimation scheme estimates the parameters and states of the system, as well as the level of the observed and process noise variances. This approach is more extensive than the one that was applied to the same system in the previous work [2], in which only the Kalman filter was applied and the system was restricted to some specific constraints.
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