Towards Synthesizing Twelve-Lead Electrocardiograms from Two Asynchronous Leads
This addresses the issue of inaccurate cardiac disease diagnosis due to limited leads in wearable ECG devices, representing an incremental advancement in medical signal processing.
The paper tackles the problem of synthesizing a full 12-lead ECG from only two asynchronous leads to improve cardiac disease diagnosis with wearable devices, resulting in generated leads that resemble original ones in rhythm and amplitude while reducing noise and baseline wander, and as a data augmentation method, it improves classification performance compared to using only one or two leads.
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.