Deep-neural-network-assisted wavefront detection for the elimination of spiral-wave turbulence in cardiac-tissue models

Ventricular arrhythmias, like ventricular tachycardia (VT) and ventricular fibrillation (VF), precipitate sudden cardiac death, which is the leading cause of mortality in the industrialized world. Thus, the elimination of VT and VF is a problem of paramount importance, which is studied experimentally, theoretically, and numerically. Numerical studies use partial-differential-equation models, for cardiac tissue, which admit solutions with spiral- or broken-spiral-wave solutions that are the mathematical counterparts of VT and VF. In silico investigations of such mathematical models of cardiac tissue allow us not only to explore the properties of such spiral-wave turbulence, but also to develop mathematical analogs of low-amplitude defibrillation by the application of currents that can eliminate spiral waves. We develop an efficient deep-neural-network based method for the control of spiral-wave turbulence in mathematical models of cardiac tissue. Specifically, we use the simple, two-variable Aliev-Panfilov and the ionically realistic TP06 mathematical models to show that the lower the correlation length ξ for spiral-turbulence patterns, the easier it is to eliminate them by the application of control currents on a mesh electrode. We then use spiral-turbulence patterns from the TP06 model to train a U-Net to predict the sodium current, which is most prominent along thin lines that track the propagating front of a spiral wave. We apply currents, in the vicinities of the predicted sodium-current lines to eliminate spiral waves efficiently. The amplitudes of these currents are adjusted automatically so that they are small when ξ is large and vice versa.