Reaction-diffusion informed approach to determine myocardial ischemia using stochastic in-silico ECGs and CNNs.

Every year, nine million people die globally from ischemic heart disease (IHD). There are many methods of early detection of IHD which can help prevent death, but few are able to determine the configuration and severity of this disease. Our study aims to determine the severity and configuration of ischemic zones by implementing the reaction-diffusion analysis of cardiac excitation in a model of the left ventricle of the human heart. Initially, this model is applied to compute twenty thousand in-silico ECG signals with stochastic distribution of ischemic parameters. Furthermore, generated data is effectively (r2=0.85) implemented for training a one-dimensional convolutional neural network to determine the severity and configuration of ischemia using only two lead surface ECG. Our results readily demonstrate that using a minimally configured portable ECG system can be instrumental for monitoring IHD and allowing early tracking of acute ischemic events.