PhysioNet/CinC Challenges


Quick links for this year's Challenge:

Please post questions and comments in the forum. However, if your question reveals information about your entry, then please email challenge at We may post parts of our reply publicly if we feel that all Challengers should benefit from it. We will not answer emails about the Challenge to any other address.

The PhysioNet/Computing in Cardiology Challenges

For the past 22 years, PhysioNet and Computing in Cardiology have co-hosted a series of annual challenges to tackle clinically interesting questions that are either unsolved or not well-solved.

The PhysioNet/Computing in Cardiology Challenge 2021 invites participants to identify clinical diagnoses from reduced-lead ECG recordings, extending last year’s Challenge on twelve-lead ECGs.

We ask participants to design and implement working, open-source algorithms that can, based only on the provided clinical data, automatically identify any cardiac abnormalities present in an ECG recording. The winners of the Challenge will be the teams whose algorithms achieve the highest score for hidden test sets of twelve-lead, six-lead, four-lead, three-lead, or two-lead ECG recordings.

Please check the below links for information about current and past Challenges, including important details about scoring and test data for previous Challenges.

Current Challenge

Past Challenges

Supported by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) under NIH grant R01EB030362.