Rotor Localization and Phase Mapping of Cardiac Excitation Waves Using Deep Neural Networks

The analysis of electrical impulse phenomena in cardiac muscle tissue is important for the diagnosis of heart rhythm disorders and other cardiac pathophysiology. Cardiac mapping techniques acquire local temporal measurements and combine them to visualize the spread of electrophysiological wave phenomena across the heart surface. However, low spatial resolution, sparse measurement locations, noise and other artifacts make it challenging to accurately visualize spatio-temporal activity. For instance, electro-anatomical catheter mapping is severely limited by the sparsity of the measurements, and optical mapping is prone to noise and motion artifacts. In the past, several approaches have been proposed to create more reliable maps from noisy or sparse mapping data. Here, we demonstrate that deep learning can be used to compute phase maps and detect phase singularities in optical mapping videos of ventricular fibrillation, as well as in very noisy, low-resolution and extremely sparse simulated data of reentrant wave chaos mimicking catheter mapping data. The self-supervised deep learning approach is fundamentally different from classical phase mapping techniques. Rather than encoding a phase signal from time-series data, a deep neural network instead learns to directly associate phase maps and the positions of phase singularities with short spatio-temporal sequences of electrical data. We tested several neural network architectures, based on a convolutional neural network (CNN) with an encoding and decoding structure, to predict phase maps or rotor core positions either directly or indirectly via the prediction of phase maps and a subsequent classical calculation of phase singularities. Predictions can be performed across different data, with models being trained on one species and then successfully applied to another, or being trained solely on simulated data and then applied to experimental data. Neural networks provide a promising alternative to conventional phase mapping and rotor core localization methods. Future uses may include the analysis of optical mapping studies in basic cardiovascular research, as well as the mapping of atrial fibrillation in the clinical setting.

Correction: Rauch et al. New Features in Crystal Orientation and Phase Mapping for Transmission Electron Microscopy. Symmetry 2021, 13, 1675

The authors wish to make the following corrections to this paper [...]

Checking Aircraft Engines Adjustment

The paper presents a new approach to the process of regulating the basic parameters of a turbine jet engine. It presents a system for monitoring these parameters developed and put into operation and the creation of the so-called phase mapping of the engine speed increment. Its modular structure is described, which allows it to be adapted quite quickly to other types of aircraft engine units. Individual modules are based on mathematical descriptions from the theory of aircraft engines. The phase mapping of the engine speed indicates a dynamic change of this parameter. On this basis, the characteristic ranges and individual points of engine operation are presented. The following are examples of characteristics and their interpretation.