  The electrocardiogram (ECG) records electrical signals in a non-invasive way
to observe the condition of the heart. It consists of 12 leads that look at the
heart from different directions. Recently, various wearable devices have
enabled immediate access to the ECG without the use of wieldy equipment.
However, they only provide ECGs with one or two leads. This results in an
inaccurate diagnosis of cardiac disease. We propose a deep generative model for
ECG synthesis from two asynchronous leads to ten leads (ECGT2T). 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 by ECGT2T 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, ECGT2T improves the classification
performance of models compared with models using ECGs with a couple of leads.
