Electrocardiogram (ECG) is a widely used non-invasive diagnostic tool for heart diseases. Many studies have devised ECG analysis models (e.g., classifiers) to assist diagnosis. As an upstream task, researches have built generative models to synthesize ECG data, which are beneficial to providing training samples, privacy protection, and annotation reduction. However, previous generative methods for ECG often neither synthesized multi-view data, nor dealt with heart disease conditions. In this paper, we propose a novel disease-aware generative adversarial network for multi-view ECG synthesis called ME-GAN, which attains panoptic electrocardio representations conditioned on heart diseases and projects the representations onto multiple standard views to yield ECG signals. Since ECG manifestations of heart diseases are often localized in specific waveforms, we propose a new "mixup normalization" to inject disease information precisely into suitable locations. In addition, we propose a view discriminator to revert disordered ECG views into a pre-determined order, supervising the generator to obtain ECG representing correct view characteristics. Besides, a new metric, rFID, is presented to assess the quality of the synthesized ECG signals. Comprehensive experiments verify that our ME-GAN performs well on multi-view ECG signal synthesis with trusty morbid manifestations.

翻译：心电图（ECG）是一种广泛用于心脏疾病诊断的非侵入性工具。已有许多研究设计了ECG分析模型（如分类器）以辅助诊断。作为上游任务，研究者构建了生成模型来合成ECG数据，这有助于提供训练样本、保护隐私并减少标注工作。然而，先前的ECG生成方法通常既无法合成多视角数据，也无法处理心脏疾病条件。本文提出了一种新颖的疾病感知生成对抗网络ME-GAN用于多视角ECG合成，该方法在心脏疾病条件下学习全景心电表征，并将表征投影到多个标准视角以生成ECG信号。由于心脏疾病的ECG表现通常局限于特定波形，我们提出了一种新的"混和归一化"方法，将疾病信息精确注入合适位置。此外，我们设计了一个视角判别器将紊乱的ECG视角恢复至预定顺序，从而监督生成器获得正确视角特征的ECG。同时，我们提出了一种新指标rFID以评估合成ECG信号的质量。综合实验验证了ME-GAN在生成具有可靠病理表现的多视角ECG信号方面表现优异。