Deep-learning-assisted detection and termination of spiral and broken-spiral waves in mathematical models for cardiac tissue
Unbroken- and broken-spiral waves, in partial-differential-equation (PDE) models for cardiac tissue, are the mathematical analogs of life-threatening cardiac arrhythmias, namely, ventricular tachycardia and ventricular-fibrillation. We develop (a) a deep-learning method for the detection of unbroken- and broken-spiral waves and (b) the elimination of such waves, e.g., by the application of low-amplitude control currents in the cardiac-tissue context. Our method is based on a convolutional neural network (CNN) that we train to distinguish between patterns with spiral-waves S and without spiral-waves NS . We obtain these patterns by carrying out extensive direct numerical simulations of PDE models for cardiac tissue in which the transmembrane potential V , when portrayed via pseudocolor plots, displays patterns of electrical activation of types S and NS . We then utilize our trained CNN to obtain, for a given pseudocolor image of V , a heatmap that has high intensity in the regions where this image shows the cores of spiral waves and the associated wavefronts. Given this heatmap, we show how to apply low-amplitude currents of a two-dimensional Gaussian profile to eliminate spiral-waves efficiently. Our in silico results are of direct relevance to the detection and elimination of these arrhythmias because our elimination of unbroken or broken-spiral waves is the mathematical analog of low-amplitude defibrillation.