Estimation of ARMA-model parameters to describe pathological conditions in cardiovascular system models

Abstract

Abstract Cardiovascular diseases cause one of three deaths worldwide. Among these diseases, especially aortic aneurysms are a highly underestimated problem. There are some diagnostic methods known in the literature (transesophageal echocardiography, doppler sonography or CT/MRT), but none is suitable as an easilyavailable non-invasive screening method, which is inexpensive and independent of the medical examiner. Within this study we present a first step towards a novel screening method using artificial intelligence: The objective of this study is to simulate healthy and diseased conditions of cardiovascular blood flow by means of numerical models, using a distributed zero-dimensional lumped approach based on the Windkessel model, in order to regard pressure-pressure transfer functions between two systemic measurement locations. The coefficients of the transfer function were estimated by an AutoRegressive-MovingAverage (ARMA)-model. The numerical estimation of the ARMA-coefficients ( a k , b k ) of order l = 45 was performed via a Subspace Gauss-Newton search method. The ARMA-coefficients were estimated using artificial zero-mean signals from the arteria brachialis and femoralis in four cases: Besides the control group, the estimations were performed on signals of two aneurysms located in the thoracic (TAA1 and TAA2) and one in the abdominal aorta (AAA). Finally, we quantified the difference between the estimated coefficients in each pathological case, using a distance measure based on the mean and the standard deviation. The largest deviation between the pathological conditions and the control group was found for the coefficients a 2 , a 3 , a 4 and a 7 . The findings suggest a reasonable situation to distinguish the pathological state of the four underlying pathological cases from the estimated coefficients; therefore we propose to diagnose the pathological states from the control group using a classification algorithm.