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

ABSTRACT:

Objective

Vast 12-lead ECGs repositories provide opportunities to develop new machine learning approaches for creating accurate and automatic diagnostic systems for cardiac abnormalities. However, most 12-lead ECG classification studies are trained, tested, or developed in single, small, or relatively homogeneous datasets. In addition, most algorithms focus on identifying small numbers of cardiac arrhythmias that do not represent the complexity and difficulty of ECG interpretation. This work addresses these issues by providing a standard, multi-institutional database and a novel scoring metric through a public competition: the PhysioNet/Computing in Cardiology Challenge 2020.

Approach

A total of 66 361 12-lead ECG recordings were sourced from six hospital systems from four c

SUBMITTER: Perez Alday EA 

PROVIDER: S-EPMC8015789 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature