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]](http://www.onlinejacc.org/content/49/10/1109.abstract). The early and
correct diagnosis of cardiac abnormalities can increase the chances of
successful treatments
[[2]](https://www.ahajournals.org/doi/full/10.1161/strokeaha.107.181486).
However, manual interpretation of the electrocardiogram is time-consuming, and
requires skilled personnel with a high degree of training
[[3]](https://www.magonlinelibrary.com/doi/abs/10.12968/bjca.2019.14.3.123).

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.