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A phase mapping is an approach to processing signals of electrograms that are recorded from the surface of cardiac tissue. The main concept of the phase mapping is an 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 of a 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 applicability of the proposed approaches in the analysis of the complex non-stationary periodic activity of the excitable cardiac tissue.

Phase Mapping for Cardiac Unipolar Electrograms with Neural Network Instead of Phase Transformation