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Generating training examples for supervised tasks is a long sought after goalin AI. We study the problem of heart signal electrocardiogram (ECG) synthesisfor improved heartbeat classification. ECG synthesis is challenging: thegeneration of training examples for such biological-physiological systems isnot straightforward, due to their dynamic nature in which the various parts ofthe system interact in complex ways. However, an understanding of thesedynamics has been developed for years in the form of mathematical processsimulators. We study how to incorporate this knowledge into the generativeprocess by leveraging a biological simulator for the task of ECGclassification. Specifically, we use a system of ordinary differentialequations representing heart dynamics, and incorporate this ODE system into theoptimization process of a generative adversarial network to create biologicallyplausible ECG training examples. We perform empirical evaluation and show thatheart simulation knowledge during the generation process improves ECGclassification.

SimGANs: Simulator-Based Generative Adversarial Networks for ECG Synthesis to Improve Deep ECG Classification