ECG classification using deep CNN improved by wavelet transform

Zhao, Y; Cheng, J; Zhang, P; Peng, X

ECG classification using deep CNN improved by wavelet transform

Zhao, Y Cheng, J Zhang, P Peng, X

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Publication Type:: Journal Article
Citation:: Computers, Materials and Continua, 2020, 64, (3), pp. 1615-1628
Issue Date:: 2020-06-30

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Field	Value	Language
dc.contributor.author	Zhao, Y
dc.contributor.author	Cheng, J
dc.contributor.author	Zhang, P
dc.contributor.author	Peng, X https://orcid.org/0000-0002-8901-1472
dc.date.accessioned	2020-09-29T07:29:00Z
dc.date.available	2020-09-29T07:29:00Z
dc.date.issued	2020-06-30
dc.identifier.citation	Computers, Materials and Continua, 2020, 64, (3), pp. 1615-1628
dc.identifier.issn	1546-2218
dc.identifier.issn	1546-2226
dc.identifier.uri	http://hdl.handle.net/10453/142924
dc.description.abstract	© 2020 Tech Science Press. All rights reserved. Atrial fibrillation is the most common persistent form of arrhythmia. A method based on wavelet transform combined with deep convolutional neural network is applied for automatic classification of electrocardiograms. Since the ECG signal is easily inferred, the ECG signal is decomposed into 9 kinds of subsignals with different frequency scales by wavelet function, and then wavelet reconstruction is carried out after segmented filtering to eliminate the influence of noise. A 24-layer convolution neural network is used to extract the hierarchical features by convolution kernels of different sizes, and finally the softmax classifier is used to classify them. This paper applies this method of the ECG data set provided by the 2017 PhysioNet/CINC challenge. After cross validation, this method can obtain 87.1% accuracy and the F1 score is 86.46%. Compared with the existing classification method, our proposed algorithm has higher accuracy and generalization ability for ECG signal data classification.
dc.language	en
dc.relation.ispartof	Computers, Materials and Continua
dc.relation.isbasedon	10.32604/cmc.2020.09938
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0103 Numerical and Computational Mathematics, 0912 Materials Engineering, 0915 Interdisciplinary Engineering
dc.subject.classification	Applied Mathematics
dc.title	ECG classification using deep CNN improved by wavelet transform
dc.type	Journal Article
utslib.citation.volume	64
utslib.for	0103 Numerical and Computational Mathematics
utslib.for	0912 Materials Engineering
utslib.for	0915 Interdisciplinary Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
dc.date.updated	2020-09-29T07:28:54Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	64
utslib.citation.issue	3

Abstract:

© 2020 Tech Science Press. All rights reserved. Atrial fibrillation is the most common persistent form of arrhythmia. A method based on wavelet transform combined with deep convolutional neural network is applied for automatic classification of electrocardiograms. Since the ECG signal is easily inferred, the ECG signal is decomposed into 9 kinds of subsignals with different frequency scales by wavelet function, and then wavelet reconstruction is carried out after segmented filtering to eliminate the influence of noise. A 24-layer convolution neural network is used to extract the hierarchical features by convolution kernels of different sizes, and finally the softmax classifier is used to classify them. This paper applies this method of the ECG data set provided by the 2017 PhysioNet/CINC challenge. After cross validation, this method can obtain 87.1% accuracy and the F1 score is 86.46%. Compared with the existing classification method, our proposed algorithm has higher accuracy and generalization ability for ECG signal data classification.

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http://hdl.handle.net/10453/142924