  The electrocardiogram (ECG) records electrical signals in a non-invasive way
to observe the condition of the heart, typically looking at the heart from 12
different directions. Several types of the cardiac disease are diagnosed by
using 12-lead ECGs Recently, various wearable devices have enabled immediate
access to the ECG without the use of wieldy equipment. However, they only
provide ECGs with a couple of leads. This results in an inaccurate diagnosis of
cardiac disease due to lacking of required leads. We propose a deep generative
model for ECG synthesis from two asynchronous leads to ten leads. It first
represents a heart condition referring to two leads, and then generates ten
leads based on the represented heart condition. Both the rhythm and amplitude
of leads generated resemble those of the original ones, while the technique
removes noise and the baseline wander appearing in the original leads. As a
data augmentation method, our model improves the classification performance of
models compared with models using ECGs with only one or two leads.
