Toward online, noninvasive, nonlinear assessment of cerebral autoregulation

Online estimation of cerebral autoregulation (CA) may be advantageous in neurosurgical and neurointensive care units. Data from transcranial Doppler, and continuous arterial blood pressure are readily available at high temporal resolution and may be used to assess CA. There are currently no methods for nonlinear, noninvasive, online assessment of CA. We frame the assessment of CA as a parameter estimation problem, in which we estimate the parameters of a nonlinear mathematical model of CA using the ensemble Kalman filter (EnKF). In this simulation study, we use the EnKF to estimate the parameters of a model of cerebral hemodynamics which predicts intracranial pressure and cerebral blood flow velocity, generated from real patient arterial blood pressure measurements. We examine the flexibility and appropriateness of the EnKF for CA assessment.