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CLC number: TP18; R540.4+1

On-line Access: 2019-04-09

Received: 2017-06-22

Revision Accepted: 2018-01-10

Crosschecked: 2019-03-14

Cited: 0

Clicked: 1207

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Xiao-guang Zhou

http://orcid.org/0000-0002-1829-927X

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Frontiers of Information Technology & Electronic Engineering  2019 Vol.20 No.3 P.405-413

10.1631/FITEE.1700413


A novel method based on convolutional neural networks for deriving standard 12-lead ECG from serial 3-lead ECG


Author(s):  Lu-di Wang, Wei Zhou, Ying Xing, Na Liu, Mahmood Movahedipour, Xiao-guang Zhou

Affiliation(s):  Automation School, Beijing University of Posts and Telecommunications, Beijing 100876, China; more

Corresponding email(s):   zxg_bupt@126.com

Key Words:  Convolutional neural networks (CNNs), Electrocardiogram (ECG) synthesis, E-health


Lu-di Wang, Wei Zhou, Ying Xing, Na Liu, Mahmood Movahedipour, Xiao-guang Zhou. A novel method based on convolutional neural networks for deriving standard 12-lead ECG from serial 3-lead ECG[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(3): 405-413.

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journal="Frontiers of Information Technology & Electronic Engineering",
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publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1700413"
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%A Ying Xing
%A Na Liu
%A Mahmood Movahedipour
%A Xiao-guang Zhou
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%I Zhejiang University Press & Springer
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T1 - A novel method based on convolutional neural networks for deriving standard 12-lead ECG from serial 3-lead ECG
A1 - Lu-di Wang
A1 - Wei Zhou
A1 - Ying Xing
A1 - Na Liu
A1 - Mahmood Movahedipour
A1 - Xiao-guang Zhou
J0 - Frontiers of Information Technology & Electronic Engineering
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DOI - 10.1631/FITEE.1700413


Abstract: 
Reconstruction of a 12-lead electrocardiogram (ECG) from a serial 3-lead ECG has been researched in the past to satisfy the need for more wearing comfort and ambulatory situations. The accuracy and real-time performance of traditional methods need to be improved. In this study, we present a novel method based on convolutional neural networks (CNNs) for the synthesis of missing precordial leads. The results show that the proposed method receives better similarity and consumes less time using the PTB database. Particularly, the presented method shows outstanding performance in reconstructing the pathological ECG signal, which is crucial for cardiac diagnosis. Our CNN-based method is shown to be more accurate and time-saving for deployment in non-hospital situations to synthesize a standard 12-lead ECG from a reduced lead-set ECG recording. This is promising for real cardiac care.

This article has been corrected, see doi:10.1631/FITEE.17e0413

一种基于卷积神经网络从3导联心电图推导标准12导联心电图的新方法

摘要:为满足人们佩戴舒适性和行走环境的需求,研究人员对从3导联心电图重建12导联心电图(electrocardiogram,ECG)方法进行了一系列研究。然而,传统方法精度和实时性有待提高。本文提出一种基于卷积神经网络(convolutional neural network,CNN)的导联重构方法。使用PTB数据库进行实验分析,结果表明,该方法重构的心电信号与真实信号之间具有较高相似性和训练效率。该方法在重建病理性心电信号时的表现优于传统算法,对心脏诊断具有重要意义。该方法能够在院外环境下部署,并且能够从较少导联心电图合成标准12导联心电图,对于心脏护理具有重要意义。

关键词:卷积神经网络(CNNs);心电图重构;电子健康

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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