论文标题
Simgans：基于模拟器的生成对抗网络，用于ECG合成以改善深层ECG分类
SimGANs: Simulator-Based Generative Adversarial Networks for ECG Synthesis to Improve Deep ECG Classification
论文作者
论文摘要
为监督任务生成培训示例是AI中长期以来一直追求的目标。我们研究心脏信号心电图（ECG）合成的问题，以改善心跳分类。心电图合成具有挑战性：这种生物生理系统的培训示例的产生并不简单，因为它们的动态性质，系统的各个部分以复杂的方式相互作用。但是，对这些动力学的理解已经以数学过程模拟器的形式发展了多年。我们通过利用生物模拟器来实现ECG分类的任务来研究如何将这些知识纳入生成过程。具体而言，我们使用代表心脏动力学的普通微分方程系统，并将此ODE系统纳入生成对抗网络的优化过程中，以创建生物学上合理的ECG培训示例。我们进行经验评估，并表明在生成过程中的心脏模拟知识可以改善ECG分类。
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.
