Cardiovascular system identification: Simulation study using arterial and central venous pressures.

Abstract

The paper presents a study of the identifiability of a lumped model of the cardiovascular system. The significance of this work from the existing literature is in the potential advantage of using both arterial and central venous (CVP) pressures, two signals that are frequently monitored in the critical care unit. The analysis is done on the system's state-space representation via control theory and system identification techniques. Non-parametric state-space identification is preferred over other identification techniques as it optimally assesses the order of a model, which best describes the input-output data, without any prior knowledge about the system. In particular, a recent system identification algorithm, namely Observer Kalman Filter Identification with Deterministic Projection, is used to identify a simplified version of an existing cardiopulmonary model. The outcome of the study highlights the following two facts. In the deterministic (noiseless) case, the theoretical indicators report that the model is fully identifiable, whereas the stochastic case reveals the difficulty in determining the complete system's dynamics. This suggests that even with the use of CVP as an additional pressure signal, the identification of a more detailed (high order) model of the circulatory system remains a challenging task.