Automated evaluation of ECG parameters during the COVID-19 pandemic

Abstract

Algorithms for prompt automated evaluation of electrocardiogram parameters in the absence of specialized equipment and specialized specialists are considered. The patient’s electrocardiogram is recorded on a paper tape, then it is photographed on the primary care doctor’s mobile phone and processed by a specialized application. The application digitizes the photographed image of the electrocardiogram, evaluates its main parameters as well as calculates criteria for the differential diagnosis of certain diseases using approximate formulas. In addition, the digitized electrocardiogram image is transmitted to the server and processed using a machine learning system. Algorithms for digitizing and analyzing an electrocardiogram have been developed that make it possible to evaluate its elements that are important for diagnosis, and the average error in determining the position of the most complex (smoothed) peaks — P and T waves — was no more than 0.1 mm. An algorithm for the criteria analysis of an electrocardiogram is proposed to support the differential diagnosis of acute myocardial infarction with ST segment elevation and early ventricular repolarization syndrome which provides accuracy values of 0.85 and F-scores of 0.74. An alternative algorithm based on a deep neural network is proposed which provides the best values — 0.96 and 0.88, respectively, but requires large computing resources and is executed on the server. The algorithms are implemented as a set of library functions. They can be used both independently and as part of a full-scale clinical decision support system for automated evaluation of electrocardiogram parameters based on a client-server architecture. In addition, all calculation results, together with a photograph of the original electrocardiogram, can be promptly transferred to a qualified cardiologist in order to receive an advisory opinion remotely.