Abstract
PurposeThis paper aims to introduce a non-invasive and convenient method to detect a life-threatening disease called aortic dissection. A Bayesian inference based on enhanced multi-sensors impedance cardiography (ICG) method has been applied to classify signals from healthy and sick patients.Design/methodology/approachA 3D numerical model consisting of simplified organ geometries is used to simulate the electrical impedance changes in the ICG-relevant domain of the human torso. The Bayesian probability theory is used for detecting an aortic dissection, which provides information about the probabilities for both cases, a dissected and a healthy aorta. Thus, the reliability and the uncertainty of the disease identification are found by this method and may indicate further diagnostic clarification.FindingsThe Bayesian classification shows that the enhanced multi-sensors ICG is more reliable in detecting aortic dissection than conventional ICG. Bayesian probability theory allows a rigorous quantification of all uncertainties to draw reliable conclusions for the medical treatment of aortic dissection.Originality/valueThis paper presents a non-invasive and reliable method based on a numerical simulation that could be beneficial for the medical management of aortic dissection patients. With this method, clinicians would be able to monitor the patient’s status and make better decisions in the treatment procedure of each patient.
Highlights
Aortic dissection (AD) is a hazardous aortic disease with high mortality
Artificial data Z have been generated for a sick patient with specific patient parameters ðRTL 1⁄4 1:65cm; radius of the false lumen (RFL) 1⁄4 0:9cm; aFL 1⁄4 3:275rad; H 1⁄4 0:45Þ, to which 4 different Gaussian noise levels have been added
A 3D numerical simulation model is developed in this work to detect aortic dissection in a human body
Summary
Aortic dissection (AD) is a hazardous aortic disease with high mortality. The fluid-dynamical forces separate the layers of the aortic wall, resulting in the formation of a true lumen and a false lumen (Figure 1) (Silaschi et al, 2017). The false lumen represents the blood-filled space between the dissected layers of the aortic wall, whereas the true lumen is the usual passageway of blood. The symptoms of AD patients are sudden severe chest or upper back pain, which are not assignable to this disease. The feasibility of impedance cardiography (ICG) in the identification of AD has been investigated in Badeli et al (2020), Reinbacher-Köstinger et al (2019). It was concluded that monitoring the ICG signal could be an asset for detecting or tracking the disease’s development, such as false lumen expansion
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