This article explores the problem of analyzing signals obtained ex vivo from rat hearts using a multi-electron array. Existing analytical methods have significant drawbacks, including low efficiency on noisy or non-stationary data and poor scalability in high-dimensional spaces. This article describes the application of the developed software package to electrogram segmentation to identify moments of cardiac tissue activation, enabling the assessment of coronary blood flow, myocardial metabolism, and cardiac contractile function under the influence of various drugs. Neural network architectures such as UNet, its modification UNet++, and SegNet were developed, adapted, and analyzed as basic signal segmentation methods. Training was conducted using a proprietary dataset of signals in which an expert marked the areas of suspected activation moments. The dataset itself consists of signals recorded from 64 electrodes on the surface of ten rat hearts, with durations ranging from 2 to 10 min.