Challenge Open Access

# Will Two Do? Varying Dimensions in Electrocardiography: the PhysioNet - Computing in Cardiology Challenge 2021

Published: Feb. 25, 2021. Version: 1.02

Reyna, M., Sadr, N., Gu, A., Perez Alday, E. A., Liu, C., Seyedi, S., Shah, A., & Clifford, G. (2021). Will Two Do? Varying Dimensions in Electrocardiography: the PhysioNet - Computing in Cardiology Challenge 2021 (version 1.02). PhysioNet. https://doi.org/10.13026/jz9p-0m02.

Perez Alday EA, Gu A, Shah AJ, Robichaux C, Wong AI, Liu C, Liu F, Rad AB, Elola A, Seyedi S, Li Q, Sharma A, Clifford GD, Reyna MA. Classification of 12-lead ECGs: the PhysioNet/Computing in Cardiology Challenge 2020. Physiol Meas. 2020 Nov 11. http://doi.org/10.1088/1361-6579/abc960.

Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220.

## Abstract

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, reduced-lead ECG systems hold promise, with evidence that subsets of the standard twelve leads can capture useful information and can 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.

## Objective

The goal of the 2021 Challenge is to identify clinical diagnoses from twelve-lead, six-lead (I, II, III, aVL, aVR, and aVF), three-lead (I, II, and V2) and two-lead (II and V5) ECG recordings.

We ask participants to design and implement a working, open-source algorithm that, based only on the provided twelve-lead ECG recordings and routine demographic data, can automatically identify any cardiac abnormalities present in the recording. We will award prizes for the top performing twelve-lead algorithm and the top performing two-lead and six-lead algorithms.

For more details and updates about the objectives of this year’s Challenge, please see https://physionetchallenges.org/2021/. This webpage will be updated after the end of the Challenge.

## Participation

We invite participants from academia, industry, and elsewhere to participate in the Challenge. Like previous years, the Challenge has both an unofficial phase and an official phase than run over the course of several months, culminating with Computing in Cardiology on 12-15 September 2021. For more details and updates about participating in this year’s Challenge, including important rules and deadlines, please see https://physionetchallenges.org/2021/.

## Data Description

We have obtained twelve-lead ECG recordings and labels from diverse sources for the public training and hidden test sets for the Challenge. We have posted multiple databases of twelve-lead ECG recordings and labels as public training sets and sequester databases of twelve-lead, six-lead, and three-lead, and two-lead ECG recordings as private test sets. For more details about the data for this year’s Challenge, please see https://physionetchallenges.org/2021/.

## Evaluation

To better capture the importance of correctly identifying cardiac abnormalities, we defined an evaluation metric to score participant algorithms by assigning different weights to different classes and classification errors. For more details about the evaluation metric for this year’s Challenge, please see https://physionetchallenges.org/2021/.

## Conflicts of Interest

The authors have no conflicts of interest to declare.

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wget -r -N -c -np https://physionet.org/files/challenge-2021/1.02/