Development of a Bayesian belief network for the diagnosis of ventricular arrhythmias

Abstract

Ventricular extrasystoles (VE) are considered the most dangerous type of heart rhythm disorders for human life, their timely detection, diagnosis and prevention are urgent issues of cardiology. In order to ensure the objectivity of diagnosis of VE, it is necessary to process a large amount of information related to the results of various medical studies, tests, anamnesis, accompanying diseases, etc., along with a long-term Holter ECG monitor. In order to process such a large amount of information and make a correct diagnosis, the issue of applying medical expert systems (MES) to doctors is currently relevant. ESs using probabilistic models based on Bayes' theorem are currently preferred because there are uncertainties in medical diagnosis issues that the same symptoms may be related to different diseases. The object of this study is the development and construction of a Bayesian belief network (BBN) for the purpose of diagnosing VEs. The choice of BBN is justified by the fact that they have the ability to combine several types of information, as well as the ability to manage uncertainties and work with incomplete information. The result of the application of the developed BBN is a probabilistic assessment of the diagnosis of VE. This network was built in the NETICA system from Norsys Software Corp. A distinctive feature of this work is that when compiling the table of conditional probabilities of BBN for the diagnosis of VE, together with the results of ECG and echo-ECG studies, data on the influence of additional factors that play a role in the occurrence of VE were used, such as the index of oxygen saturation of erythrocytes in the blood, changes in the thickness of the intima-media layer of the aortic artery and the amount of lipid fractions of blood plasma