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Arrhythmia Classification Algorithm Based on a Two-Dimensional Image and Modified EfficientNet
The classification and identification of arrhythmias using electrocardiogram (ECG) signals are of great practical significance in the early prevention and diagnosis of cardiovascular diseases. In this study, we propose an arrhythmia classification algorithm based on two-dimensional (2D) images and m...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9440778/ https://www.ncbi.nlm.nih.gov/pubmed/36065367 http://dx.doi.org/10.1155/2022/8683855 |
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author | Zhao, Cui-fang Yao, Wan-yun Yi, Mei-juan Wan, Chao Tian, Yong-le |
author_facet | Zhao, Cui-fang Yao, Wan-yun Yi, Mei-juan Wan, Chao Tian, Yong-le |
author_sort | Zhao, Cui-fang |
collection | PubMed |
description | The classification and identification of arrhythmias using electrocardiogram (ECG) signals are of great practical significance in the early prevention and diagnosis of cardiovascular diseases. In this study, we propose an arrhythmia classification algorithm based on two-dimensional (2D) images and modified EfficientNet. First, we developed a method for converting original one-dimensional (1D) ECG signals into 2D image signals. In contrast with the existing classification method that uses only the time-domain features of a 1D ECG signal, the classification of 2D images can consider the spatiotemporal characteristics of the signal. Then, to better assign feature weights, we introduced an attention feature fusion module (AFF) into the EfficientNet network to replace the addition operation in the mobile inverted bottleneck convolution (MBConv) structure of the network. We selected EfficientNet for modification because, compared with most convolutional neural networks (CNNs), EfficientNet does not require manual adjustment of parameters, which improves the accuracy and speed of the network. Finally, we combined the 2D images and the improved EfficientNet network and tested its performance as an arrhythmia classification method. Our experimental results show that the network training of the proposed method requires less equipment and training time, and this method can effectively distinguish eight types of heartbeats in the MIT-BIH arrhythmia database, with a classification accuracy of 99.54%. Thus, the model has a good classification effect. |
format | Online Article Text |
id | pubmed-9440778 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-94407782022-09-04 Arrhythmia Classification Algorithm Based on a Two-Dimensional Image and Modified EfficientNet Zhao, Cui-fang Yao, Wan-yun Yi, Mei-juan Wan, Chao Tian, Yong-le Comput Intell Neurosci Research Article The classification and identification of arrhythmias using electrocardiogram (ECG) signals are of great practical significance in the early prevention and diagnosis of cardiovascular diseases. In this study, we propose an arrhythmia classification algorithm based on two-dimensional (2D) images and modified EfficientNet. First, we developed a method for converting original one-dimensional (1D) ECG signals into 2D image signals. In contrast with the existing classification method that uses only the time-domain features of a 1D ECG signal, the classification of 2D images can consider the spatiotemporal characteristics of the signal. Then, to better assign feature weights, we introduced an attention feature fusion module (AFF) into the EfficientNet network to replace the addition operation in the mobile inverted bottleneck convolution (MBConv) structure of the network. We selected EfficientNet for modification because, compared with most convolutional neural networks (CNNs), EfficientNet does not require manual adjustment of parameters, which improves the accuracy and speed of the network. Finally, we combined the 2D images and the improved EfficientNet network and tested its performance as an arrhythmia classification method. Our experimental results show that the network training of the proposed method requires less equipment and training time, and this method can effectively distinguish eight types of heartbeats in the MIT-BIH arrhythmia database, with a classification accuracy of 99.54%. Thus, the model has a good classification effect. Hindawi 2022-08-27 /pmc/articles/PMC9440778/ /pubmed/36065367 http://dx.doi.org/10.1155/2022/8683855 Text en Copyright © 2022 Cui-fang Zhao 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 Zhao, Cui-fang Yao, Wan-yun Yi, Mei-juan Wan, Chao Tian, Yong-le Arrhythmia Classification Algorithm Based on a Two-Dimensional Image and Modified EfficientNet |
title | Arrhythmia Classification Algorithm Based on a Two-Dimensional Image and Modified EfficientNet |
title_full | Arrhythmia Classification Algorithm Based on a Two-Dimensional Image and Modified EfficientNet |
title_fullStr | Arrhythmia Classification Algorithm Based on a Two-Dimensional Image and Modified EfficientNet |
title_full_unstemmed | Arrhythmia Classification Algorithm Based on a Two-Dimensional Image and Modified EfficientNet |
title_short | Arrhythmia Classification Algorithm Based on a Two-Dimensional Image and Modified EfficientNet |
title_sort | arrhythmia classification algorithm based on a two-dimensional image and modified efficientnet |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9440778/ https://www.ncbi.nlm.nih.gov/pubmed/36065367 http://dx.doi.org/10.1155/2022/8683855 |
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