Title :
Nonlinear open-loop gain of the baroreflex using artificial feedforward neural networks
Author :
Larchie, I. ; Nugent, S.T. ; Finley, J.P.
Author_Institution :
Dept. of Electr. Eng., Tech. Univ. Nova Scotia, Halifax, NS, Canada
Abstract :
The baroreflex control system is inherently nonlinear. Clinical measurements which rely on linear models provide adequate representation of the system as long as the input perturbations to the reflex loop are small and fall within the linear region of the response curve. The authors propose a novel technique that combines approximation power of a class of artificial neural networks (ANNs), Volterra nonlinear block representation and eigen-analysis to provide estimates of the open-loop gain of the baroreflex. A range of eigen-parameters are extracted from the converged weight matrices of the ANN to provide a range of possible values of the gain factor of simulated baroreflex response curve
Keywords :
backpropagation; biocontrol; cardiology; feedforward neural nets; haemodynamics; physiological models; transfer functions; Volterra nonlinear block representation; approximation power; artificial feedforward neural networks; baroreflex control system; eigen-analysis; gain factor; nonlinear open-loop gain; Artificial neural networks; Baroreflex; Control systems; Feedforward neural networks; Heart rate; Least squares approximation; Neural networks; Open loop systems; Pediatrics; Power system modeling;
Conference_Titel :
Electrical and Computer Engineering, 1995. Canadian Conference on
Conference_Location :
Montreal, Que.
Print_ISBN :
0-7803-2766-7
DOI :
10.1109/CCECE.1995.528189