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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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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. |
format | Online Article Text |
id | pubmed-7813686 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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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