An approach to early stage detection of atherosclerosis using arterial blood pressure measurements

Abstract

Atherosclerosis is a pathological condition that develops gradually over the years and may eventually lead to a heart attack, a stroke, or a peripheral vascular disease, depending upon its site of occurrence in the human arterial network. We aim to detect this pathological condition in its early stage so that the necessary measures can be taken timely. To achieve this, a third-order nonlinear model of the cardiovascular system is considered, having states as systemic arterial and venous pressures along with the left ventricular volume. The available measurement signal is arterial blood pressure taken from the radial artery. This paper proposes the idea of online tracking of model parameters by utilizing an unscented Kalman filter (UKF) based framework that would help monitor the above-mentioned pathological condition. Furthermore, a classification approach has been presented, which carries out screening of subjects suffering from atherosclerotic cardiovascular diseases (CVDs) while utilizing estimates obtained from the UKF framework. It is observed that clinical quantities such as arterial compliance, systolic blood pressure, and ventricular elastance play an important role in the development of atherosclerosis. The classification results are quite encouraging. The proposed framework regularly monitors the atherosclerotic condition and has a potential for the early-stage screening of subjects suffering from atherosclerosis. With an increase in sedentary lifestyle in modern world, an early-stage screening of atherosclerotic cardiovascular diseases would be an important contribution to the healthcare and biomedical community.