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

Wenxiao Jia, Xiao Xu, Xian Xu, Yuyao Sun, Xiaoshuang Liu
{"title":"Arrhythmia Detection and Classification of 12-lead ECGs Using a Deep Neural Network","authors":"Wenxiao Jia, Xiao Xu, Xian Xu, Yuyao Sun, Xiaoshuang Liu","doi":"10.22489/cinc.2020.035","DOIUrl":null,"url":null,"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.","PeriodicalId":165296,"journal":{"name":"2020 Computing in Cardiology Conference (CinC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 Computing in Cardiology Conference (CinC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.22489/cinc.2020.035","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6

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.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于深度神经网络的12导联心电图心律失常检测与分类
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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Morphological Changes of Seismocardiogram Due to Different Sensor Placements on the Sternum Arrhythmia Detection and Classification of 12-lead ECGs Using a Deep Neural Network Post-Processing of Electrocardiographic Imaging Signals to Identify Atrial Fibrillation Drivers
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1