title: Estimation of hidden state variables of the Intracranial system using constrained nonlinear Kalman filters.
abstract: Impeded by the rigid skull, assessment of physiological variables of the intracranial system is difficult. A hidden state estimation approach is used in the present work to facilitate the estimation of unobserved variables from available clinical measurements including intracranial pressure (ICP) and cerebral blood flow velocity (CBFV). The estimation algorithm is based on a modified nonlinear intracranial mathematical model, whose parameters are first identified in an offline stage using a nonlinear optimization paradigm. Following the offline stage, an online filtering process is performed using a nonlinear Kalman filter (KF)-like state estimator that is equipped with a new way of deriving the Kalman gain satisfying the physiological constraints on the state variables. The proposed method is then validated by comparing different state estimation methods and input/output (I/O) configurations using simulated data. It is also applied to a set of CBFV, ICP and arterial blood pressure (ABP) signal segments from brain injury patients. The results indicated that the proposed constrained nonlinear KF achieved the best performance among the evaluated state estimators and that the state estimator combined with the I/O configuration that has ICP as the measured output can potentially be used to estimate CBFV continuously. Finally, the state estimator combined with the I/O configuration that has both ICP and CBFV as outputs can potentially estimate the lumped cerebral arterial radii, which are not measurable in a typical clinical environment.
authors.affiliation: Brain Monitoring and Modeling Laboratory, Division of Neurosurgery, University of California, Los Angeles, CA 90034, USA. xiaohu@ucla.edu
authors.forename: Xiao
authors.initials: X
authors.lastname: Hu
authors.forename: Valeriy
authors.initials: V
authors.lastname: Nenov
authors.forename: Marvin
authors.initials: M
authors.lastname: Bergsneider
authors.forename: Thomas C
authors.initials: TC
authors.lastname: Glenn
authors.forename: Paul
authors.initials: P
authors.lastname: Vespa
authors.forename: Neil
authors.initials: N
authors.lastname: Martin
mesh_terms: D000222:Adaptation, Physiological / Q000502:physiology; D000465:Algorithms; D001783:Blood Flow Velocity / Q000502:physiology; D001794:Blood Pressure / Q000502:physiology; D001921:Brain / Q000098:blood supply* / Q000502:physiology*; D002560:Cerebrovascular Circulation / Q000502:physiology*; D003198:Computer Simulation; D005246:Feedback / Q000502:physiology; D006706:Homeostasis / Q000502:physiology; D006801:Humans; D007427:Intracranial Pressure / Q000502:physiology*; D008955:Models, Cardiovascular; D008959:Models, Neurological; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D013269:Stochastic Processes