LightX3ECG: A Lightweight and eXplainable Deep Learning System for 3-lead Electrocardiogram Classification

TL;DR

A novel deep learning system is developed to accurately identify multiple cardiovascular abnormalities by using only three ECG leads, which can make ECG more prevalent as it can be conveniently recorded by portable or wearable devices.

Abstract

Cardiovascular diseases (CVDs) are a group of heart and blood vessel disorders that is one of the most serious dangers to human health, and the number of such patients is still growing. Early and accurate detection plays a key role in successful treatment and intervention. Electrocardiogram (ECG) is the gold standard for identifying a variety of cardiovascular abnormalities. In clinical practices and most of the current research, standard 12-lead ECG is mainly used. However, using a lower number of leads can make ECG more prevalent as it can be conveniently recorded by portable or wearable devices. In this research, we develop a novel deep learning system to accurately identify multiple cardiovascular abnormalities by using only three ECG leads.

Figures (24)

• Figure 1.1: The usual structure of an ECG signal.
• Figure 1.2: An overview of the proposed system. Dashed arrows indicate the interpreting stage.
• Figure 3.1: The architecture of 1D-SEResNet backbones.
• Figure 3.2: The proposed Lead-wise Attention module.
• Figure 4.1: The explanation for a sample SNR ECG recording.