Arrhythmia Detection and Classification of 12-lead ECGs Using a Deep Neural Network

Abstract

Electrocardiogram (ECG) plays a critical role in the clinical diagnoses, and the algorithmic paradigm of deep learning present an opportunity to improve the accuracy and scalability of arrhythmia detection and classification. The goal of the 2020 Challenge is to identify clinical diagnoses from 12-lead ECG recordings. And the training set consists of 6,877 (male: 3,699; female: 3,178) 12-ECG recordings lasting from 6 seconds to 60 seconds.