  Generating training examples for supervised tasks is a long sought after goal
in AI. We study the problem of heart signal electrocardiogram (ECG) synthesis
for improved heartbeat classification. ECG synthesis is challenging: the
generation of training examples for such biological-physiological systems is
not straightforward, due to their dynamic nature in which the various parts of
the system interact in complex ways. However, an understanding of these
dynamics has been developed for years in the form of mathematical process
simulators. We study how to incorporate this knowledge into the generative
process by leveraging a biological simulator for the task of ECG
classification. Specifically, we use a system of ordinary differential
equations representing heart dynamics, and incorporate this ODE system into the
optimization process of a generative adversarial network to create biologically
plausible ECG training examples. We perform empirical evaluation and show that
heart simulation knowledge during the generation process improves ECG
classification.
