A Deep-learning Method for Detecting and Terminating Spiral Waves in Mathematical Models for Cardiac Tissue

Mahesh Kumar Mulimani1, Jaya KUmar Alageshan2, Rahul Pandit3
1PhD student, Department of Physics, Indian Institute of Science, 2Post Doctoral Fellow, Department of Physics, Indian Institute of Science, 3Professor, Department of Physics, Indian Institute of Science


The development of low-amplitude defibrillation schemes, for the elimination pathological electrical waves of activation, in cardiac tissue, is a major challenge in the treatment of life-threatening cardiac arrhythmias. We com- bine the dataset generated from extensive direct numerical simulations (DNS) using mathematical models for cardiac tissue with a deep-learning method to construct a new defibrillation scheme to eliminate unbroken and broken spiral waves, which are the mathematical analogs of ventricular tachycardia and ventricular fibrillation. We use a convolution neural network (CNN), where we train the CNN to distinguish spiral-wave patterns (S) from those that do not have spiral waves (N S). For the training, the dataset we use is ' 25000 different pseudocolor plots of the transmembrane potential Vm obtained from DNS of both two-variable and ionically realistic models for cardiac tissue.

We have checked that our trained CNN can, distinguish between patterns: with spirals, and patterns that do not have spirals. Next, we show how of the broken spiral waves, which has high intensity in regions with spiral-wave cores; this heatmap plays a central role in our new defibrillation scheme: We first show that a control (defibrillation) current with a two-dimensional (2D) Gaussian profile (with width σ ' 75% of linear size of our simulation domain) eliminates the spiral wave when we apply it exactly at the spiral core. We then demonstrate that the defibrillation-current profile, comprising of 2D Gaussians on a square lattice, whose amplitudes are proportional to the heat-map intensity at a given point in our simulation domain, eliminates broken spiral waves.