Use of deep learning algorithms for segmentation of microelectrode arrays electrograms in the study of changes in myocardial bioelectrical activity

Abstract

The using of modern electrophysiological methods during scientific research implies processing of huge data sets which becomes a significant problem. Progress in the development of equipment for registration of multiple local field potentials from the tissue surface contributes to the study of spatio-temporal characteristics of myocardial bioelectrical activity at a more detailed level. But it also raises the need for automation of data analysis, which can be accomplished by algorithms based on machine learning. New scientific approaches can contribute to the discovery of new facts about substances widely used but with under-researched efficacy. The paper presents the results of epicardial mapping by flexible microelectrode arrays of myocardium of isolated rat heart perfused with a solution containing L-carnitine, nutritional supplement that may be recommended for people suffering from cardiovascular disease. Electrograms from microelectrode arrays were analyzed using a neural network model based on U-Net architecture adapted for segmentation of one-dimensional signal. It was shown that the presence of L-carnitine in the perfusion solution caused a decrease in heart rate, myocardial excitation conduction velocity, coronary blood flow intensity of the isolated rat heart and suppressed physiological responses of the heart to adrenaline stimulation.