Full Text:
<2634>
Summary: 
<1780>
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
Clicked: 6751
A novel method based on convolutional neural networks for deriving standard 12-lead ECG from serial 3-lead ECG
| ||||||||||||||
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
[1]Addison PS, 2005. Wavelet transforms and the ECG: a review. Physiol Meas, 26(5):R155-R199.
[2]Atoui H, Fayn J, Rubel P, 2004. A neural network approach for patient-specific 12-lead ECG synthesis in patient monitoring environments. Proc Computers in Cardiology, p.161-164.
[3]Atoui H, Fayn J, Rubel P, 2010. A novel neural-network model for deriving standard 12-lead ECGs from serial three-lead ECGs: application to self-care. IEEE Trans Inform Technol Biomed, 14(3):883-890.
[4]Bojarski M, Del Testa D, Dworakowski D, et al., 2016. End to end learning for self-driving cars. https://arxiv.org/abs/1604.07316
[5]Bousseljot R, Kreiseler D, Schnabel, A, 1995. Nutzung der EKG-Signaldatenbank CARDIODAT der PTB über das Internet. Biomedizinische Technik, Band 40, Ergänzungsband 1, S 317.
[6]Ciregan D, Meier U, Schmidhuber J, 2012. Multi-column deep neural networks for image classification. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.3642- 3649.
[7]Drew BJ, Pelter MM, Wung SF, et al., 1999. Accuracy of the EASI 12-lead electrocardiogram compared to the standard 12-lead electrocardiogram for diagnosing multiple cardiac abnormalities. J Electrocardiol, 32(S1):38-47.
[8]Duin RPW, 2000. Learned from neural networks. Proc 6th Annual Conf of the Advanced School for Computing and Imaging, p.9-13.
[9]Ettl S, Kaminski J, Knauer MC, et al., 2008. Shape reconstruction from gradient data. Appl Opt, 47(12):2091-2097.
[10]Frank E, 1956. An accurate, clinically practical system for spatial vectorcardiography. Circulation, 13(5):737-749.
[11]Gacsádi A, Szolgay P, 2010. Variational computing based segmentation methods for medical imaging by using CNN. Proc 12th Int Workshop on Cellular Nanoscale Networks and Their Applications, p.1-6.
[12]Goldberger AL, Amaral LAN, Glass L, et al., 2000. Physio- Bank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation, 101(23):e215-e220.
[13]Gulrajani RM, 1998. The forward and inverse problems of electrocardiography. IEEE Eng Med Biol Mag, 17(5): 84-101.
[14]Horácek BM, Warren JW, Feild DQ, et al., 2002. Statistical and deterministic approaches to designing transformations of electrocardiographic leads. J Electrocardiol, 35(4):41-52.
[15]Hubel DH, Wiesel TN, 1959. Receptive fields of single neurones in the cat’s striate cortex. J Physiol, 148(3):574-591.
[16]Kors JA, van Herpen G, 2010. Computer analysis of the electrocardiogram. In: Macfarlane PW, van Oosterom A, Pahlm O, et al. (Eds.), Comprehensive Electrocardiology. Springer, London, p.1721-1765.
[17]Krizhevsky A, Sutskever I, Hinton GE, 2012. ImageNet classification with deep convolutional neural networks. Proc 25th Int Conf on Neural Information Processing Systems, p.1097-1105.
[18]LeCun Y, Boser B, Denker JS, et al., 1990. Handwritten digit recognition with a back-propagation network. In: Touretzky DS (Ed.), Advances in Neural Information Processing Systems. Morgan Kaufmann Publishers Inc., San Francisco, USA, p.396-404.
[19]Lee H, Grosse R, Ranganath R, et al., 2009. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. Proc 26th Annual Int Conf on Machine Learning, p.609-616.
[20]Lu C, Wang ZY, Zhou B, 2017. Intelligent fault diagnosis of rolling bearing using hierarchical convolutional network based health state classification. Adv Eng Inform, 32: 139-151.
[21]Modre R, Seger M, Fischer G, et al., 2006. Cardiac anisotropy: is it negligible regarding noninvasive activation time imaging. IEEE Trans Biomed Eng, 53(4):569-580.
[22]Nelwan SP, 2005. Evaluation of 12-Lead Electrocardiogram Reconstruction Methods for Patient Monitoring. PhD Thesis, Erasmus University Rotterdam, Rotterdam, Holland.
[23]Nelwan SP, Meij SH, 2006. Derived 12-lead ECG systems. J Electrocardiol, 39(1):29-30.
[24]Nelwan SP, Kors JA, Meij SH, et al., 2004. Reconstruction of the 12-lead electrocardiogram from reduced lead sets. J Electrocardiol, 37(1):11-18.
[25]Palm RB, 2012. Prediction as a Candidate for Learning Deep Hierarchical Models of Data. MS Thesis, Technical University of Denmark, Lyngby, Denmark.
[26]Pan JP, Tompkins WJ, 1985. A real-time QRS detection algorithm. IEEE Trans Biomed Eng, BME-32(3):230-236.
[27]Scherer JA, Jenkins JM, Nicklas JM, 1990. Synthesis of the 12-lead electrocardiogram from a 3-lead subset using patient-specific transformation vectors: an algorithmic approach to computerized signal synthesis. J Electrocardiol, 22(S1):128.
[28]Serre T, Wolf L, Bileschi S, et al., 2007. Robust object recognition with cortex-like mechanisms. IEEE Trans Patt Anal Mach Intell, 29(3):411-426.
[29]Shin HC, Roth HR, Gao MC, et al., 2016. Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imag, 35(5):1285-1298.
[30]Srivastava N, Hinton G, Krizhevsky A, et al., 2014. Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res, 15(1):1929-1958.
[31]Tomašić I, Trobec R, 2014. Electrocardiographic systems with reduced numbers of leads—synthesis of the 12-lead ECG. IEEE Rev Biomed Eng, 7:126-142.
[32]Zhang QX, Zhou D, Zeng X, 2016. A novel machine learning-enabled framework for instantaneous heart rate monitoring from motion-artifact-corrupted electrocardiogram signals. Physiol Meas, 37(11):1945-1967.
[33]Zhang QX, Zeng X, Hu WC, et al., 2017a. A machine learning- empowered system for long-term motion-tolerant wearable monitoring of blood pressure and heart rate with ear-ECG/PPG. IEEE Access, 5:10547-10561.
[34]Zhang QX, Zhou D, Zeng X, 2017b. HeartID: a multiresolution convolutional neural network for ECG-based biometric human identification in smart health applications. IEEE Access, 5:11805-11816.
[35]Zhang QX, Zhou D, Zeng X, 2017c. Machine learning- empowered biometric methods for biomedicine applications. AIMS Med Sci, 4(3):274-290.
[36]Zhang QX, Zhou D, Zeng X, 2017d. A novel framework for motion-tolerant instantaneous heart rate estimation by phase-domain multiview dynamic time warping. IEEE Trans Biomed Eng, 64(11):2562-2574.
ORCID: 
Open peer comments: Debate/Discuss/Question/Opinion
<1>