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

Towards Synthesizing Twelve-Lead Electrocardiograms from Two Asynchronous Leads