title: Nonlinear identification of the total baroreflex arc: higher-order nonlinearity.
abstract: The total baroreflex arc is the open-loop system relating carotid sinus pressure (CSP) to arterial pressure (AP). The nonlinear dynamics of this system were recently characterized. First, Gaussian white noise CSP stimulation was employed in open-loop conditions in normotensive and hypertensive rats with sectioned vagal and aortic depressor nerves. Nonparametric system identification was then applied to measured CSP and AP to establish a second-order nonlinear Uryson model. The aim in this study was to assess the importance of higher-order nonlinear dynamics via development and evaluation of a third-order nonlinear model of the total arc using the same experimental data. Third-order Volterra and Uryson models were developed by employing nonparametric and parametric identification methods. The R2values between the AP predicted by the best third-order Volterra model and measured AP in response to Gaussian white noise CSP not utilized in developing the model were 0.69 ± 0.03 and 0.70 ± 0.03 for normotensive and hypertensive rats, respectively. The analogous R2values for the best third-order Uryson model were 0.71 ± 0.03 and 0.73 ± 0.03. These R2values were not statistically different from the corresponding values for the previously established second-order Uryson model, which were both 0.71 ± 0.03 (P > 0.1). Furthermore, none of the third-order models predicted well-known nonlinear behaviors including thresholding and saturation better than the second-order Uryson model. Additional experiments suggested that the unexplained AP variance was partly due to higher brain center activity. In conclusion, the second-order Uryson model sufficed to represent the sympathetically mediated total arc under the employed experimental conditions.
authors.affiliation: Department of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan.
authors.forename: Mohsen
authors.initials: M
authors.lastname: Moslehpour
authors.affiliation: Department of Cardiovascular Dynamics, National Cerebral and Cardiovascular Center, Osaka, Japan; and.
authors.forename: Toru
authors.initials: T
authors.lastname: Kawada
authors.affiliation: Department of Cardiovascular Medicine, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
authors.forename: Kenji
authors.initials: K
authors.lastname: Sunagawa
authors.affiliation: Department of Cardiovascular Dynamics, National Cerebral and Cardiovascular Center, Osaka, Japan; and.
authors.forename: Masaru
authors.initials: M
authors.lastname: Sugimachi
authors.affiliation: Department of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan; rama@egr.msu.edu.
authors.forename: Ramakrishna
authors.initials: R
authors.lastname: Mukkamala
mesh_terms: D000818:Animals; D017704:Baroreflex / Q000502:physiology*; D001794:Blood Pressure / Q000502:physiology*; D002346:Carotid Sinus / Q000502:physiology*; D003198:Computer Simulation; D006801:Humans; D006973:Hypertension / Q000503:physiopathology*; D008955:Models, Cardiovascular; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D011311:Pressoreceptors / Q000502:physiology*; D051381:Rats
keywords: Gaussian white noise; arterial baroreflex; higher-order nonlinear model; hypertension; system identification