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A particle swarm optimization improved BP neural network intelligent model for electrocardiogram classification

BACKGROUND: As proven to reflect the work state of heart and physiological situation objectively, electrocardiogram (ECG) is widely used in the assessment of human health, especially the diagnosis of heart disease. The accuracy and reliability of abnormal ECG (AECG) decision depend to a large extent...

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Autores principales: Li, Guixiang, Tan, Zhongwei, Xu, Weikang, Xu, Fei, Wang, Lei, Chen, Jun, Wu, Kai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8322832/
https://www.ncbi.nlm.nih.gov/pubmed/34330266
http://dx.doi.org/10.1186/s12911-021-01453-6
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author Li, Guixiang
Tan, Zhongwei
Xu, Weikang
Xu, Fei
Wang, Lei
Chen, Jun
Wu, Kai
author_facet Li, Guixiang
Tan, Zhongwei
Xu, Weikang
Xu, Fei
Wang, Lei
Chen, Jun
Wu, Kai
author_sort Li, Guixiang
collection PubMed
description BACKGROUND: As proven to reflect the work state of heart and physiological situation objectively, electrocardiogram (ECG) is widely used in the assessment of human health, especially the diagnosis of heart disease. The accuracy and reliability of abnormal ECG (AECG) decision depend to a large extent on the feature extraction. However, it is often uneasy or even impossible to obtain accurate features, as the detection process of ECG is easily disturbed by the external environment. And AECG got many species and great variation. What’s more, the ECG result obtained after a long time past, which can not reach the purpose of early warning or real-time disease diagnosis. Therefore, developing an intelligent classification model with an accurate feature extraction method to identify AECG is of quite significance. This study aimed to explore an accurate feature extraction method of ECG and establish a suitable model for identifying AECG and the diagnosis of heart disease. METHODS: In this research, the wavelet combined with four operations and adaptive threshold methods were applied to filter the ECG and extract its feature waves first. Then, a BP neural network (BPNN) intelligent model and a particle swarm optimization (PSO) improved BPNN (PSO-BPNN) intelligent model based on MIT-BIH open database was established to identify ECG. To reduce the complexity of the model, the principal component analysis (PCA) was used to minimize the feature dimension. RESULTS: Wavelet transforms combined four operations and adaptive threshold methods were capable of ECG filtering and feature extraction. PCA can significantly deduce the modeling feature dimension to minimize the complexity and save classification time. The PSO-BPNN intelligent model was suitable for identifying five types of ECG and showed better effects while comparing it with the BPNN model. CONCLUSION: In summary, it was further concluded that the PSO-BPNN intelligent model would be a suitable way to identify AECG and provide a tool for the diagnosis of heart disease.
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spelling pubmed-83228322021-07-30 A particle swarm optimization improved BP neural network intelligent model for electrocardiogram classification Li, Guixiang Tan, Zhongwei Xu, Weikang Xu, Fei Wang, Lei Chen, Jun Wu, Kai BMC Med Inform Decis Mak Research BACKGROUND: As proven to reflect the work state of heart and physiological situation objectively, electrocardiogram (ECG) is widely used in the assessment of human health, especially the diagnosis of heart disease. The accuracy and reliability of abnormal ECG (AECG) decision depend to a large extent on the feature extraction. However, it is often uneasy or even impossible to obtain accurate features, as the detection process of ECG is easily disturbed by the external environment. And AECG got many species and great variation. What’s more, the ECG result obtained after a long time past, which can not reach the purpose of early warning or real-time disease diagnosis. Therefore, developing an intelligent classification model with an accurate feature extraction method to identify AECG is of quite significance. This study aimed to explore an accurate feature extraction method of ECG and establish a suitable model for identifying AECG and the diagnosis of heart disease. METHODS: In this research, the wavelet combined with four operations and adaptive threshold methods were applied to filter the ECG and extract its feature waves first. Then, a BP neural network (BPNN) intelligent model and a particle swarm optimization (PSO) improved BPNN (PSO-BPNN) intelligent model based on MIT-BIH open database was established to identify ECG. To reduce the complexity of the model, the principal component analysis (PCA) was used to minimize the feature dimension. RESULTS: Wavelet transforms combined four operations and adaptive threshold methods were capable of ECG filtering and feature extraction. PCA can significantly deduce the modeling feature dimension to minimize the complexity and save classification time. The PSO-BPNN intelligent model was suitable for identifying five types of ECG and showed better effects while comparing it with the BPNN model. CONCLUSION: In summary, it was further concluded that the PSO-BPNN intelligent model would be a suitable way to identify AECG and provide a tool for the diagnosis of heart disease. BioMed Central 2021-07-30 /pmc/articles/PMC8322832/ /pubmed/34330266 http://dx.doi.org/10.1186/s12911-021-01453-6 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Li, Guixiang
Tan, Zhongwei
Xu, Weikang
Xu, Fei
Wang, Lei
Chen, Jun
Wu, Kai
A particle swarm optimization improved BP neural network intelligent model for electrocardiogram classification
title A particle swarm optimization improved BP neural network intelligent model for electrocardiogram classification
title_full A particle swarm optimization improved BP neural network intelligent model for electrocardiogram classification
title_fullStr A particle swarm optimization improved BP neural network intelligent model for electrocardiogram classification
title_full_unstemmed A particle swarm optimization improved BP neural network intelligent model for electrocardiogram classification
title_short A particle swarm optimization improved BP neural network intelligent model for electrocardiogram classification
title_sort particle swarm optimization improved bp neural network intelligent model for electrocardiogram classification
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8322832/
https://www.ncbi.nlm.nih.gov/pubmed/34330266
http://dx.doi.org/10.1186/s12911-021-01453-6
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