Classification of 12-lead ECGs: The PhysioNet/Computing in Cardiology Challenge 2020

The electrocardiogram (ECG) is a non-invasive representation of the electrical activity of the heart from electrodes placed on the surface of the torso. The standard 12-lead ECG has been widely used to diagnose a variety of cardiac abnormalities such as cardiac arrhythmias, and predicts cardiovascular morbidity and mortality [1]. The early and correct diagnosis of cardiac abnormalities can increase the chances of successful treatments [2]. However, manual interpretation of the electrocardiogram is time-consuming, and requires skilled personnel with a high degree of training [3].

Automatic detection and classification of cardiac abnormalities can assist physicians in the diagnosis of the growing number of ECGs recorded. Over the last decade, there have been increasing numbers of attempts to stimulate 12-lead ECG classification. Many of these algorithms seem to have the potential for accurate identification of cardiac abnormalities. However, most of these methods have only been tested or developed in single, small, or relatively homogeneous datasets. The PhysioNet/Computing in Cardiology Challenge 2020 provides an opportunity to address this problem by providing data from a wide set of sources.

心电图（ECG）系指通过放置在躯干表面的电极记录的心脏电活动的一种非侵入性表示。标准的12导联心电图已被广泛用于诊断各种心脏异常，如心律失常，并预测心血管的发病率和死亡率[1]。对心脏异常的早期和准确诊断可以增加治疗成功的几率[2]。然而，心电图的手动解读耗时且需要具备高度训练的专业人员[3]。自动检测和分类心脏异常能够协助医师诊断日益增长的记录心电图数量。在过去十年中，对12导联心电图分类的尝试日益增多。许多此类算法似乎具有准确识别心脏异常的潜力。然而，这些方法中的大多数仅在一小部分、相对同质化的数据集上进行过测试或开发。PhysioNet/心血管计算挑战赛2020提供了利用来自广泛来源的数据解决这一问题的机会。