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Unbroken and broken spiral waves, in partial-differential-equation (PDE)models for cardiac tissue, are the mathematical analogs of life-threateningcardiac arrhythmias, namely, ventricular tachycardia (VT) andventricular-fibrillation (VF). We develop a (a) deep-learning method for thedetection of unbroken and broken spiral waves and (b) the elimination of suchwaves, e.g., by the application of low-amplitude control currents in thecardiac-tissue context. Our method is based on a convolutional neural network(CNN) that we train to distinguish between patterns with spiral waves S andwithout spiral waves NS. We obtain these patterns by carrying out extensivedirect numerical simulations (DNS) of PDE models for cardiac tissue in whichthe transmembrane potential V, when portrayed via pseudocolor plots, displayspatterns of electrical activation of types S and NS. We then utilize ourtrained CNN to obtain, for a given pseudocolor image of V, a heat map that hashigh intensity in the regions where this image shows the cores of spiral waves.Given this heat map, we show how to apply low-amplitude Gaussian current pulsesto eliminate spiral waves efficiently. Our in silico results are of directrelevance to the detection and elimination of these arrhythmias because ourelimination of unbroken or broken spiral waves is the mathematical analog oflow-amplitude defibrillation.

Deep-learning-assisted detection and termination of spiral- and broken-spiral waves in mathematical models for cardiac tissue