Towards Synthesizing Twelve-Lead Electrocardiograms from Two Asynchronous Leads

TL;DR

This paper introduces a deep generative model that synthesizes a full 12-lead ECG from only two asynchronous leads, enhancing diagnostic accuracy and aiding data augmentation for machine learning models.

Contribution

The novel deep generative approach enables accurate synthesis of 12-lead ECGs from minimal lead data, improving diagnosis and model training.

Findings

Generated ECGs closely resemble original signals in rhythm and amplitude.

The model reduces noise and baseline wander in synthesized ECGs.

Data augmentation with synthesized ECGs improves classification performance.

Abstract

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.