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Wavelet Scattering Transform for ECG Beat Classification
An electrocardiogram (ECG) records the electrical activity of the heart; it contains rich pathological information on cardiovascular diseases, such as arrhythmia. However, it is difficult to visually analyze ECG signals due to their complexity and nonlinearity. The wavelet scattering transform can g...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7568798/ https://www.ncbi.nlm.nih.gov/pubmed/33133225 http://dx.doi.org/10.1155/2020/3215681 |
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author | Liu, Zhishuai Yao, Guihua Zhang, Qing Zhang, Junpu Zeng, Xueying |
author_facet | Liu, Zhishuai Yao, Guihua Zhang, Qing Zhang, Junpu Zeng, Xueying |
author_sort | Liu, Zhishuai |
collection | PubMed |
description | An electrocardiogram (ECG) records the electrical activity of the heart; it contains rich pathological information on cardiovascular diseases, such as arrhythmia. However, it is difficult to visually analyze ECG signals due to their complexity and nonlinearity. The wavelet scattering transform can generate translation-invariant and deformation-stable representations of ECG signals through cascades of wavelet convolutions with nonlinear modulus and averaging operators. We proposed a novel approach using wavelet scattering transform to automatically classify four categories of arrhythmia ECG heartbeats, namely, nonectopic (N), supraventricular ectopic (S), ventricular ectopic (V), and fusion (F) beats. In this study, the wavelet scattering transform extracted 8 time windows from each ECG heartbeat. Two dimensionality reduction methods, principal component analysis (PCA) and time window selection, were applied on the 8 time windows. These processed features were fed to the neural network (NN), probabilistic neural network (PNN), and k-nearest neighbour (KNN) classifiers for classification. The 4th time window in combination with KNN (k = 4) has achieved the optimal performance with an averaged accuracy, positive predictive value, sensitivity, and specificity of 99.3%, 99.6%, 99.5%, and 98.8%, respectively, using tenfold cross-validation. Thus, our proposed model is capable of highly accurate arrhythmia classification and will provide assistance to physicians in ECG interpretation. |
format | Online Article Text |
id | pubmed-7568798 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-75687982020-10-30 Wavelet Scattering Transform for ECG Beat Classification Liu, Zhishuai Yao, Guihua Zhang, Qing Zhang, Junpu Zeng, Xueying Comput Math Methods Med Research Article An electrocardiogram (ECG) records the electrical activity of the heart; it contains rich pathological information on cardiovascular diseases, such as arrhythmia. However, it is difficult to visually analyze ECG signals due to their complexity and nonlinearity. The wavelet scattering transform can generate translation-invariant and deformation-stable representations of ECG signals through cascades of wavelet convolutions with nonlinear modulus and averaging operators. We proposed a novel approach using wavelet scattering transform to automatically classify four categories of arrhythmia ECG heartbeats, namely, nonectopic (N), supraventricular ectopic (S), ventricular ectopic (V), and fusion (F) beats. In this study, the wavelet scattering transform extracted 8 time windows from each ECG heartbeat. Two dimensionality reduction methods, principal component analysis (PCA) and time window selection, were applied on the 8 time windows. These processed features were fed to the neural network (NN), probabilistic neural network (PNN), and k-nearest neighbour (KNN) classifiers for classification. The 4th time window in combination with KNN (k = 4) has achieved the optimal performance with an averaged accuracy, positive predictive value, sensitivity, and specificity of 99.3%, 99.6%, 99.5%, and 98.8%, respectively, using tenfold cross-validation. Thus, our proposed model is capable of highly accurate arrhythmia classification and will provide assistance to physicians in ECG interpretation. Hindawi 2020-10-09 /pmc/articles/PMC7568798/ /pubmed/33133225 http://dx.doi.org/10.1155/2020/3215681 Text en Copyright © 2020 Zhishuai Liu et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Liu, Zhishuai Yao, Guihua Zhang, Qing Zhang, Junpu Zeng, Xueying Wavelet Scattering Transform for ECG Beat Classification |
title | Wavelet Scattering Transform for ECG Beat Classification |
title_full | Wavelet Scattering Transform for ECG Beat Classification |
title_fullStr | Wavelet Scattering Transform for ECG Beat Classification |
title_full_unstemmed | Wavelet Scattering Transform for ECG Beat Classification |
title_short | Wavelet Scattering Transform for ECG Beat Classification |
title_sort | wavelet scattering transform for ecg beat classification |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7568798/ https://www.ncbi.nlm.nih.gov/pubmed/33133225 http://dx.doi.org/10.1155/2020/3215681 |
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