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

Journal Article

AbstractThe subject of the PhysioNet/Computing in Cardiology Challenge 2020 was the identification of cardiac abnormalities in 12-lead electrocardiogram (ECG) recordings. A total of 66,405 recordings were sourced from hospital systems from four distinct countries and annotated with clinical diagnoses, including 43,101 annotated recordings that were posted publicly.For this Challenge, we asked participants to design working, open-source algorithms for identifying cardiac abnormalities in 12-lead ECG recordings. This Challenge provided several innovations. First, we sourced data from multiple institutions from around the world with different demographics, allowing us to assess the generalizability of the algorithms. Second, we required participants to submit both their trained models and the code for reproducing their trained models from the training data, which aids the generalizability and reproducibility of the algorithms. Third, we proposed a novel evaluation metric that considers different misclassification errors for different cardiac abnormalities, reflecting the clinical reality that some diagnoses have similar outcomes and varying risks.Over 200 teams submitted 850 algorithms (432 of which successfully ran) during the unofficial and official phases of the Challenge, representing a diversity of approaches from both academia and industry for identifying cardiac abnormalities. The official phase of the Challenge is ongoing.

Full Text

Duke Authors

Cited Authors

  • Alday, EAP; Gu, A; Shah, A; Robichaux, C; Wong, A-KI; Liu, C; Liu, F; Rad, AB; Elola, A; Seyedi, S; Li, Q; Sharma, A; Clifford, GD; Reyna, MA

Digital Object Identifier (DOI)

  • 10.1101/2020.08.11.20172601