title: Regional cerebral blood flow estimation by neural network-based parametric regression analysis.
abstract: An artificial neural network (ANN) model was proposed for real-time estimation of regional cerebral blood flow (rCBF), by given head and expired air curves obtained through 133Xe inhalation. The network was constructed according to a regression model described by a linear differential equation. Experimental results compare well with those obtained by conventional curve fitting strategies, but the parameter estimation process is much simplified. A systematic procedure in developing ANN for parametric regression analysis was introduced; networks are constructed according to the selected regression model so that the obtained weights of a trained network directly represent parameters of the regression model which best fits the observed data set. Such a design-oriented methodology extends the classification-based applications of ANN to parametric regression analysis, and therefore may have more generalized applications besides rCBF estimation.
authors.affiliation: Department of Electrical and Computer Engineering, University of Miami, Coral Gables 33124.
authors.forename: F Y
authors.initials: FY
authors.lastname: Wu
authors.forename: J D
authors.initials: JD
authors.lastname: Slater
mesh_terms: D000465:Algorithms; D002560:Cerebrovascular Circulation / Q000502:physiology*; D002561:Cerebrovascular Disorders / Q000175:diagnosis / Q000503:physiopathology; D006801:Humans; D008432:Mathematical Computing; D008959:Models, Neurological; D016571:Neural Networks, Computer; D012016:Reference Values; D012044:Regression Analysis