Kalman filter modeling of cerebral blood flow autoregulation

M. A. Masnadi-Shirazi, K. Behbehani, R. Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Pages734-737
Number of pages4
Volume26 I
StatePublished - 2004
EventConference Proceedings - 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2004 - San Francisco, CA, United States
Duration: Sep 1 2004Sep 5 2004

Other

OtherConference Proceedings - 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2004
CountryUnited States
CitySan Francisco, CA
Period9/1/049/5/04

Keywords

  • Autoregulation
  • Cerebral Blood Flow Modeling

ASJC Scopus subject areas

  • Bioengineering

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