Will Two Do? Varying Dimensions in Electrocardiography: The PhysioNet/Computing in Cardiology Challenge 2021

The electrocardiogram (ECG) is a non-invasive representation of the electrical
activity of the heart. Although the twelve-lead ECG is the standard diagnostic
screening system for many cardiological issues, the limited accessibility of
twelve-lead ECG devices provides a rationale for smaller, lower-cost, and
easier to use devices. While single-lead ECGs are limiting
[[1](https://pubmed.ncbi.nlm.nih.gov/9740885/)], reduced-lead ECG systems hold
promise, with evidence that subsets of the standard twelve leads can capture
useful information [[2](https://pubmed.ncbi.nlm.nih.gov/12539095)],
[[3](https://www.sciencedirect.com/science/article/abs/pii/S0022073606005346)],
[[4](https://pubmed.ncbi.nlm.nih.gov/3812249)] and even be comparable to
twelve-lead ECGs in some limited contexts. In 2017 we challenged the public to
classify AF from a single-lead ECG, and in 2020 we challenged the public to
diagnose a much larger number of cardiac problems using twelve-lead
recordings. However, there is limited evidence to demonstrate the utility of
reduced-lead ECGs for capturing a wide range of diagnostic information.

In this year's Challenge, we ask the following question: **' Will two do?'**
This year's Challenge builds on [last year's
Challenge](/content/challenge-2020/)
[[5]](https://doi.org/10.1088/1361-6579/abc960), which asked participants to
classify cardiac abnormalities from twelve-lead ECGs. We are asking you to
build an algorithm that can classify cardiac abnormalities from twelve-lead,
six-lead, four-lead, three-lead, and two-lead ECGs. We will test each
algorithm on databases of these reduced-lead ECGs, and the differences in
performances of the algorithms on these databases will reveal the utility of
reduced-lead ECGs in comparison to standard twelve-lead EGCs.