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An effective frequency-domain feature of atrial fibrillation based on time–frequency analysis
BACKGROUND: Atrial fibrillation is a type of persistent arrhythmia that can lead to serious complications. Therefore, accurate and quick detection of atrial fibrillation by surface electrocardiogram has great importance on further treatment. The practical electrocardiogram signals contain various in...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7690088/ https://www.ncbi.nlm.nih.gov/pubmed/33239025 http://dx.doi.org/10.1186/s12911-020-01337-1 |
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author | Hu, Yusong Zhao, Yantao Liu, Jihong Pang, Jin Zhang, Chen Li, Peizhe |
author_facet | Hu, Yusong Zhao, Yantao Liu, Jihong Pang, Jin Zhang, Chen Li, Peizhe |
author_sort | Hu, Yusong |
collection | PubMed |
description | BACKGROUND: Atrial fibrillation is a type of persistent arrhythmia that can lead to serious complications. Therefore, accurate and quick detection of atrial fibrillation by surface electrocardiogram has great importance on further treatment. The practical electrocardiogram signals contain various interferences in different frequencies, such as myoelectricity interference, power interference and so on. Detection speed and accuracy largely depend on the atrial fibrillation signal features extracted by the algorithm. But some of the discovered atrial fibrillation features are not well distinguishable, resulting in poor classification effect. METHODS: This paper proposed a high distinguishable frequency feature—the frequency corresponding to the maximum amplitude in the frequency spectrum. We used the R–R interval detection method optimized with the mathematical morphology method and combined with the wavelet transform method for analysis. According to the two features—the maximum amplitude in the frequency spectrum and R–R interval irregular, we could recognize atrial fibrillation signals in electrocardiogram signals by decision tree classification algorithm. RESULTS: The data used in the experiment come from the MIT-BIH database, which is publicly accessible via the web and with ethical approval and consent. Based on the input of time-domain and frequency-domain features, we classified sinus rhythm signals and AF signals using the decision tree generated by classification and regression tree (CART) algorithm. From the confusion matrix, we got the accuracy was 98.9%, sensitivity was 97.93% and specificity was 99.63%. CONCLUSIONS: The experimental results can prove the validity of the maximum amplitude in the frequency spectrum and the practicability and accuracy of the detection method, which applied this frequency-domain feature. Through the detection method, we obtained good accuracy of classifying sinus rhythm signals and atrial fibrillation signals. And the sensitivity and specificity of our method were pretty good by comparison with other studies. |
format | Online Article Text |
id | pubmed-7690088 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-76900882020-11-30 An effective frequency-domain feature of atrial fibrillation based on time–frequency analysis Hu, Yusong Zhao, Yantao Liu, Jihong Pang, Jin Zhang, Chen Li, Peizhe BMC Med Inform Decis Mak Research Article BACKGROUND: Atrial fibrillation is a type of persistent arrhythmia that can lead to serious complications. Therefore, accurate and quick detection of atrial fibrillation by surface electrocardiogram has great importance on further treatment. The practical electrocardiogram signals contain various interferences in different frequencies, such as myoelectricity interference, power interference and so on. Detection speed and accuracy largely depend on the atrial fibrillation signal features extracted by the algorithm. But some of the discovered atrial fibrillation features are not well distinguishable, resulting in poor classification effect. METHODS: This paper proposed a high distinguishable frequency feature—the frequency corresponding to the maximum amplitude in the frequency spectrum. We used the R–R interval detection method optimized with the mathematical morphology method and combined with the wavelet transform method for analysis. According to the two features—the maximum amplitude in the frequency spectrum and R–R interval irregular, we could recognize atrial fibrillation signals in electrocardiogram signals by decision tree classification algorithm. RESULTS: The data used in the experiment come from the MIT-BIH database, which is publicly accessible via the web and with ethical approval and consent. Based on the input of time-domain and frequency-domain features, we classified sinus rhythm signals and AF signals using the decision tree generated by classification and regression tree (CART) algorithm. From the confusion matrix, we got the accuracy was 98.9%, sensitivity was 97.93% and specificity was 99.63%. CONCLUSIONS: The experimental results can prove the validity of the maximum amplitude in the frequency spectrum and the practicability and accuracy of the detection method, which applied this frequency-domain feature. Through the detection method, we obtained good accuracy of classifying sinus rhythm signals and atrial fibrillation signals. And the sensitivity and specificity of our method were pretty good by comparison with other studies. BioMed Central 2020-11-25 /pmc/articles/PMC7690088/ /pubmed/33239025 http://dx.doi.org/10.1186/s12911-020-01337-1 Text en © The Author(s) 2020 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/. The Creative Commons Public Domain Dedication waiver (http://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 Article Hu, Yusong Zhao, Yantao Liu, Jihong Pang, Jin Zhang, Chen Li, Peizhe An effective frequency-domain feature of atrial fibrillation based on time–frequency analysis |
title | An effective frequency-domain feature of atrial fibrillation based on time–frequency analysis |
title_full | An effective frequency-domain feature of atrial fibrillation based on time–frequency analysis |
title_fullStr | An effective frequency-domain feature of atrial fibrillation based on time–frequency analysis |
title_full_unstemmed | An effective frequency-domain feature of atrial fibrillation based on time–frequency analysis |
title_short | An effective frequency-domain feature of atrial fibrillation based on time–frequency analysis |
title_sort | effective frequency-domain feature of atrial fibrillation based on time–frequency analysis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7690088/ https://www.ncbi.nlm.nih.gov/pubmed/33239025 http://dx.doi.org/10.1186/s12911-020-01337-1 |
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