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CLC number: TP18; R540.4+1
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2019-03-14
Cited: 0
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A novel method based on convolutional neural networks for deriving standard 12-lead ECG from serial 3-lead ECG
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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.
@article{title="A novel method based on convolutional neural networks for deriving standard 12-lead ECG from serial 3-lead ECG",
author="Lu-di Wang, Wei Zhou, Ying Xing, Na Liu, Mahmood Movahedipour, Xiao-guang Zhou",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="20",
number="3",
pages="405-413",
year="2019",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1700413"
}
%0 Journal Article
%T A novel method based on convolutional neural networks for deriving standard 12-lead ECG from serial 3-lead ECG
%A Lu-di Wang
%A Wei Zhou
%A Ying Xing
%A Na Liu
%A Mahmood Movahedipour
%A Xiao-guang Zhou
%J Frontiers of Information Technology & Electronic Engineering
%V 20
%N 3
%P 405-413
%@ 2095-9184
%D 2019
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1700413
TY - JOUR
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
VL - 20
IS - 3
SP - 405
EP - 413
%@ 2095-9184
Y1 - 2019
PB - Zhejiang University Press & Springer
ER -
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
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