title: Multivariate nonstationary modeling of cerebral hemodynamics.
abstract: We extracted adaptive univariate and multivariate dynamic models of cerebral hemodynamics during resting and hypercapnic conditions using a Recursive Least Squares estimation scheme with multiple adaptive forgetting factors. The time dependent relationship between mean arterial blood pressure (MABP), end-tidal CO2 tension (PETCO2) and middle cerebral artery blood flow velocity (CBFV) was assessed using Laguerre - Volterra models with time varying coefficients. The results suggest that the addition of PETCO2 as a second input yields more accurate and less nonstationary estimates, indicating that unobservable physiological variables are important in the context of nonstationary systems modeling, and particularly for assessing cerebral hemodynamics and autoregulation.
authors.forename: Kyriaki
authors.initials: K
authors.lastname: Kostoglou
authors.forename: Chantel T
authors.initials: CT
authors.lastname: Debert
authors.forename: Marc J
authors.initials: MJ
authors.lastname: Poulin
authors.forename: Georgios D
authors.initials: GD
authors.lastname: Mitsis
mesh_terms: D001783:Blood Flow Velocity; D001794:Blood Pressure / Q000502:physiology; D002245:Carbon Dioxide / Q000378:metabolism; D002560:Cerebrovascular Circulation / Q000502:physiology*; D006439:Hemodynamics; D006706:Homeostasis; D006801:Humans; D006935:Hypercapnia / Q000503:physiopathology; D020768:Middle Cerebral Artery / Q000000981:diagnostic imaging / Q000502:physiology; D008962:Models, Theoretical; D017156:Photoplethysmography; D014463:Ultrasonography
chemical_list: D002245:Carbon Dioxide