The problem of studying, treating and preventing cardiovascular diseases is one of the most important for the modern world. Advanced technologies allow recording a huge amount of data, thereby increasing the completeness of the picture of biological processes occurring in the heart. The need to automate the analysis of experimental and medical data in the field of heart research is growing significantly. A promising approach is the use of artificial intelligence-based methods for analyzing data recorded during the study of the bioelectrical activity of the heart. The presented work describes the creation of a software package for analyzing epicardial electrograms of isolated rat hearts. The main part of the package is a segmenting neural network for localizing myocardial activation moments based on the UNet architecture. The choice of this architecture is due to its effectiveness in segmenting multidimensional data. Within the framework of the article, the basic architecture of the UNet network was modified to process one-dimensional signals. The purpose of the work was to create software that includes the following functionality: creating a data set for training, validation and testing, training a model, creating and editing markup. The program will automatically localize activation moments to speed up and improve the efficiency of the study. The developed complex is a promising solution for automating the analysis of multichannel electrograms of the heart and its structures using a segmenting neural network based on the UNet architecture and pre- and post-processing algorithms.