基于深度神经网络的12导联心电图心律失常检测与分类

2020 Computing in Cardiology Conference (CinC) Pub Date : 2020-12-30 DOI:10.22489/cinc.2020.035

Wenxiao Jia, Xiao Xu, Xian Xu, Yuyao Sun, Xiaoshuang Liu

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引用次数: 6

摘要

心电图(ECG)在临床诊断中起着至关重要的作用，而深度学习的算法范式为提高心律失常检测和分类的准确性和可扩展性提供了机会。2020年挑战的目标是从12导联心电图记录中确定临床诊断。训练集由6877人组成(男性:3699人;女性:3178)12个心电图记录，持续时间从6秒到60秒不等。

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

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.

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2020 Computing in Cardiology Conference (CinC)

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