Key result
A linear state variable model of long-term arterial blood pressure regulation yielded a performance very close to that of the actual non-linear model for analyzing system stability and dynamics.
A linear state space model can accurately approximate the non-linear cardiovascular system for analyzing long-term blood pressure regulation, potentially aiding in prediction and diagnosis of hypertension.
Linear models may simplify BP dynamics research; leaves open clinical translation for hypertension prediction.
Abnormal cardiovascular regulation acts as a potential trigger of hypertension, which is one of the main problems in modern societies. The purpose of the present work is to describe the non-linear cardiovascular system in a state variable form, suitable for applying control theory techniques. A linear state variable description of long-term Arterial Blood Pressure (ABP) regulation has been developed for the analysis of the system's properties. The influence of the system's parameters on the stability and dynamic behaviour is analysed using eigen values. The closed loop poles of the system under normal and pathological conditions have been evaluated through the root locus technique, which indicates some applications of prediction and diagnosis. The simulation results show that the linear model yields a performance which is very close to that of the actual non-linear model.
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Shahin et al. (2007) studied Hypertension. Linear state variable model vs. Non-linear model was evaluated on Model performance and dynamic behavior. A linear state variable model of long-term arterial blood pressure regulation yielded a performance very close to that of the actual non-linear model for analyzing system stability and dynamics.
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