  A phase mapping is an approach to processing signals of electrograms recorded
from the surface of cardiac tissue. The main concept of phase mapping is the
application of the phase transformation with the aim to obtain signals with
useful properties. In our study, we propose to use a simple sawtooth signal
instead of a phase signal for processing of electrogram data and building of
the phase maps. We denote transformation that can provide this signal as a
phase-like transformation (PLT). PLT defined via a convolutional neural network
that is trained on a dataset from computer models of cardiac tissue
electrophysiology. The proposed approaches were validated on data from the
detailed personalized model of the human torso electrophysiology. This paper
includes visualization of the phase map based on PLT and shows the robustness
of the proposed approaches in the analysis of the complex non-stationary
periodic activity of the excitable cardiac tissue.
