Challenge Open Access

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

Published: April 23, 2020. Version: 1.0.1

Perez Alday, E. A., Gu, A., Shah, A., Liu, C., Sharma, A., Seyedi, S., Bahrami Rad, A., Reyna, M., & Clifford, G. (2020). Classification of 12-lead ECGs: the PhysioNet - Computing in Cardiology Challenge 2020 (version 1.0.1). PhysioNet. https://doi.org/10.13026/f4ab-0814.

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 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. The early and correct diagnosis of cardiac abnormalities can increase the chances of successful treatments. However, manual interpretation of the electrocardiogram is time-consuming and requires skilled personnel with a high degree of training. Automatic detection and classification of cardiac abnormalities can assist physicians in the diagnosis of the growing number of ECGs recorded. The PhysioNet/Computing in Cardiology Challenge 2020 provides an opportunity to address this problem by providing data from a wide set of sources.

Please see https://physionetchallenges.github.io/2020/ for all information about this year's Challenge. We are using the above GitHub Pages link and Google Groups to post all updates this year. At the end of the Challenge, the current page will be updated to reflect the complete event and the final results.

Objective

The goal of the 2020 Challenge is to identify the clinical diagnosis from 12-lead ECG recordings. We ask participants to design and implement a working, open-source algorithm that can, based only on the clinical data provided, automatically identify the cardiac abnormality or abnormalities present in each 12-lead ECG recording. The winners of the Challenge will be the team whose algorithm achieves the highest score for records in the hidden test set. For more details about the objective of this year’s Challenge, please see https://physionetchallenges.github.io/2020/.

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 13-16 September 2020. For more details about participating in this year’s Challenge, including important rules and deadlines, please see https://physionetchallenges.github.io/2020/.

Data Description

We have sourced 12-lead ECG recordings and labels from multiple sources for the public training and hidden test sets for the Challenge. For more details about the data for this year’s Challenge, please see https://physionetchallenges.github.io/2020/.

Evaluation

To better capture the importance of correctly identifying cardiac abnormalities, we evaluate participant algorithms using evaluation metrics that assign different weights to different classes and classification errors. For more details about the evaluation metrics for this year’s Challenge, please see https://physionetchallenges.github.io/2020/.

Conflicts of Interest

The authors have no conflicts of interest to declare.

References

1. Details about the challenge publication will be shared later.

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