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Robust classification of heart valve sound based on adaptive EMD and feature fusion
Cardiovascular disease (CVD) is considered one of the leading causes of death worldwide. In recent years, this research area has attracted researchers’ attention to investigate heart sounds to diagnose the disease. To effectively distinguish heart valve defects from normal heart sounds, adaptive emp...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9731417/ https://www.ncbi.nlm.nih.gov/pubmed/36480575 http://dx.doi.org/10.1371/journal.pone.0276264 |
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author | Wang, Weibo Yuan, Jin Wang, Bingrong Fang, Yu Zheng, Yongkang Hu, Xingping |
author_facet | Wang, Weibo Yuan, Jin Wang, Bingrong Fang, Yu Zheng, Yongkang Hu, Xingping |
author_sort | Wang, Weibo |
collection | PubMed |
description | Cardiovascular disease (CVD) is considered one of the leading causes of death worldwide. In recent years, this research area has attracted researchers’ attention to investigate heart sounds to diagnose the disease. To effectively distinguish heart valve defects from normal heart sounds, adaptive empirical mode decomposition (EMD) and feature fusion techniques were used to analyze the classification of heart sounds. Based on the correlation coefficient and Root Mean Square Error (RMSE) method, adaptive EMD was proposed under the condition of screening the intrinsic mode function (IMF) components. Adaptive thresholds based on Hausdorff Distance were used to choose the IMF components used for reconstruction. The multidimensional features extracted from the reconstructed signal were ranked and selected. The features of waveform transformation, energy and heart sound signal can indicate the state of heart activity corresponding to various heart sounds. Here, a set of ordinary features were extracted from the time, frequency and nonlinear domains. To extract more compelling features and achieve better classification results, another four cardiac reserve time features were fused. The fusion features were sorted using six different feature selection algorithms. Three classifiers, random forest, decision tree, and K-nearest neighbor, were trained on open source and our databases. Compared to the previous work, our extensive experimental evaluations show that the proposed method can achieve the best results and have the highest accuracy of 99.3% (1.9% improvement in classification accuracy). The excellent results verified the robustness and effectiveness of the fusion features and proposed method. |
format | Online Article Text |
id | pubmed-9731417 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-97314172022-12-09 Robust classification of heart valve sound based on adaptive EMD and feature fusion Wang, Weibo Yuan, Jin Wang, Bingrong Fang, Yu Zheng, Yongkang Hu, Xingping PLoS One Research Article Cardiovascular disease (CVD) is considered one of the leading causes of death worldwide. In recent years, this research area has attracted researchers’ attention to investigate heart sounds to diagnose the disease. To effectively distinguish heart valve defects from normal heart sounds, adaptive empirical mode decomposition (EMD) and feature fusion techniques were used to analyze the classification of heart sounds. Based on the correlation coefficient and Root Mean Square Error (RMSE) method, adaptive EMD was proposed under the condition of screening the intrinsic mode function (IMF) components. Adaptive thresholds based on Hausdorff Distance were used to choose the IMF components used for reconstruction. The multidimensional features extracted from the reconstructed signal were ranked and selected. The features of waveform transformation, energy and heart sound signal can indicate the state of heart activity corresponding to various heart sounds. Here, a set of ordinary features were extracted from the time, frequency and nonlinear domains. To extract more compelling features and achieve better classification results, another four cardiac reserve time features were fused. The fusion features were sorted using six different feature selection algorithms. Three classifiers, random forest, decision tree, and K-nearest neighbor, were trained on open source and our databases. Compared to the previous work, our extensive experimental evaluations show that the proposed method can achieve the best results and have the highest accuracy of 99.3% (1.9% improvement in classification accuracy). The excellent results verified the robustness and effectiveness of the fusion features and proposed method. Public Library of Science 2022-12-08 /pmc/articles/PMC9731417/ /pubmed/36480575 http://dx.doi.org/10.1371/journal.pone.0276264 Text en © 2022 Wang et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Wang, Weibo Yuan, Jin Wang, Bingrong Fang, Yu Zheng, Yongkang Hu, Xingping Robust classification of heart valve sound based on adaptive EMD and feature fusion |
title | Robust classification of heart valve sound based on adaptive EMD and feature fusion |
title_full | Robust classification of heart valve sound based on adaptive EMD and feature fusion |
title_fullStr | Robust classification of heart valve sound based on adaptive EMD and feature fusion |
title_full_unstemmed | Robust classification of heart valve sound based on adaptive EMD and feature fusion |
title_short | Robust classification of heart valve sound based on adaptive EMD and feature fusion |
title_sort | robust classification of heart valve sound based on adaptive emd and feature fusion |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9731417/ https://www.ncbi.nlm.nih.gov/pubmed/36480575 http://dx.doi.org/10.1371/journal.pone.0276264 |
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