  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.

Machine Learning;Artificial Intelligence;Signal Processing