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Electrocardiogram (ECG) is a widely used non-invasive diagnostic tool forheart diseases. Many studies have devised ECG analysis models (e.g.,classifiers) to assist diagnosis. As an upstream task, researches have builtgenerative models to synthesize ECG data, which are beneficial to providingtraining 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 noveldisease-aware generative adversarial network for multi-view ECG synthesiscalled ME-GAN, which attains panoptic electrocardio representations conditionedon heart diseases and projects the representations onto multiple standard viewsto yield ECG signals. Since ECG manifestations of heart diseases are oftenlocalized in specific waveforms, we propose a new "mixup normalization" toinject disease information precisely into suitable locations. In addition, wepropose a view discriminator to revert disordered ECG views into apre-determined order, supervising the generator to obtain ECG representingcorrect view characteristics. Besides, a new metric, rFID, is presented toassess the quality of the synthesized ECG signals. Comprehensive experimentsverify that our ME-GAN performs well on multi-view ECG signal synthesis withtrusty morbid manifestations.

ME-GAN: Learning Panoptic Electrocardio Representations for Multi-view ECG Synthesis Conditioned on Heart Diseases