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Linear and nonlinear identification of the carotid sinus baroreflex in the very low‐frequency range

Since the arterial baroreflex system is classified as an immediate control system, the focus has been on analyzing its dynamic characteristics in the frequency range between 0.01 and 1 Hz. Although the dynamic characteristics in the frequency range below 0.01 Hz are not expected to be large, actual...

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Detalles Bibliográficos
Autores principales: Kawada, Toru, Miyamoto, Tadayoshi, Mukkamala, Ramakrishna, Saku, Keita
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9300957/
https://www.ncbi.nlm.nih.gov/pubmed/35859325
http://dx.doi.org/10.14814/phy2.15392
Descripción
Sumario:Since the arterial baroreflex system is classified as an immediate control system, the focus has been on analyzing its dynamic characteristics in the frequency range between 0.01 and 1 Hz. Although the dynamic characteristics in the frequency range below 0.01 Hz are not expected to be large, actual experimental data are scant. The aim was to identify the dynamic characteristics of the carotid sinus baroreflex in the frequency range down to 0.001 Hz. The carotid sinus baroreceptor regions were isolated from the systemic circulation, and carotid sinus pressure (CSP) was changed every 10 s according to Gaussian white noise with a mean of 120 mmHg and standard deviation of 20 mmHg for 90 min in anesthetized Wistar‐Kyoto rats (n = 8). The dynamic gain of the linear transfer function relating CSP to arterial pressure (AP) at 0.001 Hz tended to be greater than that at 0.01 Hz (1.060 ± 0.197 vs. 0.625 ± 0.067, p = 0.080), suggesting that baroreflex control was largely maintained at 0.001 Hz. Regarding nonlinear analysis, a second‐order Uryson model predicted AP with a higher R (2) value (0.645 ± 0.053) than a linear model (R (2) = 0.543 ± 0.057, p = 0.025) or a second‐order Volterra model (R (2) = 0.589 ± 0.055, p = 0.045) in testing data. These pieces of information may be used to create baroreflex models that can add a component of autonomic control to a cardiovascular digital twin for predicting acute hemodynamic responses to treatments and tailoring individual treatment strategies.