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

TL;DR

This paper introduces SimGANs, a novel approach that integrates biological heart simulators into GANs to generate realistic ECG data, enhancing deep learning-based heartbeat classification accuracy.

Contribution

It presents a method combining system of ODE-based heart dynamics with GANs to produce biologically plausible ECG signals for improved classification.

Findings

Incorporating heart simulation knowledge improves ECG classification accuracy.

The method effectively generates realistic ECG signals.

Empirical results demonstrate enhanced deep learning performance.

Abstract

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.