Comput Methods Programs Biomed - Functionality of the baroreceptor nerves in heart rate regulation.

Tópicos

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Resumo

Two models describing the afferent baroreceptor firing are analyzed, a basic model predicting firing using a single nonlinear differential equation, and an extended model, coupling K nonlinear responses. Both models respond to the the rate (derivative) and the rate history of the carotid sinus arterial pressure. As a result both the rate and the relative level of the carotid sinus arterial pressure is sensed. Simulations with these models show that responses to step changes in pressure follow from the rate sensitivity as observed in experimental studies. Adaptation and asymmetric responses are a consequence of the memory encapsulated by the models, and the nonlinearity gives rise to sigmoidal response curves. The nonlinear afferent baroreceptor models are coupled with an effector model, and the coupled model has been used to predict baroreceptor feedback regulation of heart rate during postural change from sitting to standing and during head-up tilt. The efferent model couples the afferent nerve paths to the sympathetic and parasympathetic outflow, and subsequently predicts the build up of an action potential at the sinus knot of the heart. In this paper, we analyze the nonlinear afferent model and show that the coupled model is able to predict heart rate regulation using blood pressure data as an input.

Resumo Limpo

two model describ affer baroreceptor fire analyz basic model predict fire use singl nonlinear differenti equat extend model coupl k nonlinear respons model respond rate deriv rate histori carotid sinus arteri pressur result rate relat level carotid sinus arteri pressur sens simul model show respons step chang pressur follow rate sensit observ experiment studi adapt asymmetr respons consequ memori encapsul model nonlinear give rise sigmoid respons curv nonlinear affer baroreceptor model coupl effector model coupl model use predict baroreceptor feedback regul heart rate postur chang sit stand headup tilt effer model coupl affer nerv path sympathet parasympathet outflow subsequ predict build action potenti sinus knot heart paper analyz nonlinear affer model show coupl model abl predict heart rate regul use blood pressur data input

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