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A Study on Arrhythmia via ECG Signal Classification Using the Convolutional Neural Network

Cardiovascular diseases (CVDs) are the leading cause of death today. The current identification method of the diseases is analyzing the Electrocardiogram (ECG), which is a medical monitoring technology recording cardiac activity. Unfortunately, looking for experts to analyze a large amount of ECG da...

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Autores principales: Wu, Mengze, Lu, Yongdi, Yang, Wenli, Wong, Shen Yuong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7813686/
https://www.ncbi.nlm.nih.gov/pubmed/33469423
http://dx.doi.org/10.3389/fncom.2020.564015
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author Wu, Mengze
Lu, Yongdi
Yang, Wenli
Wong, Shen Yuong
author_facet Wu, Mengze
Lu, Yongdi
Yang, Wenli
Wong, Shen Yuong
author_sort Wu, Mengze
collection PubMed
description Cardiovascular diseases (CVDs) are the leading cause of death today. The current identification method of the diseases is analyzing the Electrocardiogram (ECG), which is a medical monitoring technology recording cardiac activity. Unfortunately, looking for experts to analyze a large amount of ECG data consumes too many medical resources. Therefore, the method of identifying ECG characteristics based on machine learning has gradually become prevalent. However, there are some drawbacks to these typical methods, requiring manual feature recognition, complex models, and long training time. This paper proposes a robust and efficient 12-layer deep one-dimensional convolutional neural network on classifying the five micro-classes of heartbeat types in the MIT- BIH Arrhythmia database. The five types of heartbeat features are classified, and wavelet self-adaptive threshold denoising method is used in the experiments. Compared with BP neural network, random forest, and other CNN networks, the results show that the model proposed in this paper has better performance in accuracy, sensitivity, robustness, and anti-noise capability. Its accurate classification effectively saves medical resources, which has a positive effect on clinical practice.
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spelling pubmed-78136862021-01-18 A Study on Arrhythmia via ECG Signal Classification Using the Convolutional Neural Network Wu, Mengze Lu, Yongdi Yang, Wenli Wong, Shen Yuong Front Comput Neurosci Neuroscience Cardiovascular diseases (CVDs) are the leading cause of death today. The current identification method of the diseases is analyzing the Electrocardiogram (ECG), which is a medical monitoring technology recording cardiac activity. Unfortunately, looking for experts to analyze a large amount of ECG data consumes too many medical resources. Therefore, the method of identifying ECG characteristics based on machine learning has gradually become prevalent. However, there are some drawbacks to these typical methods, requiring manual feature recognition, complex models, and long training time. This paper proposes a robust and efficient 12-layer deep one-dimensional convolutional neural network on classifying the five micro-classes of heartbeat types in the MIT- BIH Arrhythmia database. The five types of heartbeat features are classified, and wavelet self-adaptive threshold denoising method is used in the experiments. Compared with BP neural network, random forest, and other CNN networks, the results show that the model proposed in this paper has better performance in accuracy, sensitivity, robustness, and anti-noise capability. Its accurate classification effectively saves medical resources, which has a positive effect on clinical practice. Frontiers Media S.A. 2021-01-05 /pmc/articles/PMC7813686/ /pubmed/33469423 http://dx.doi.org/10.3389/fncom.2020.564015 Text en Copyright © 2021 Wu, Lu, Yang and Wong. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Wu, Mengze
Lu, Yongdi
Yang, Wenli
Wong, Shen Yuong
A Study on Arrhythmia via ECG Signal Classification Using the Convolutional Neural Network
title A Study on Arrhythmia via ECG Signal Classification Using the Convolutional Neural Network
title_full A Study on Arrhythmia via ECG Signal Classification Using the Convolutional Neural Network
title_fullStr A Study on Arrhythmia via ECG Signal Classification Using the Convolutional Neural Network
title_full_unstemmed A Study on Arrhythmia via ECG Signal Classification Using the Convolutional Neural Network
title_short A Study on Arrhythmia via ECG Signal Classification Using the Convolutional Neural Network
title_sort study on arrhythmia via ecg signal classification using the convolutional neural network
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7813686/
https://www.ncbi.nlm.nih.gov/pubmed/33469423
http://dx.doi.org/10.3389/fncom.2020.564015
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