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, adaptiv...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0276264 |
| _version_ | 1797956097610350592 |
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| author | Weibo Wang Jin Yuan Bingrong Wang Yu Fang Yongkang Zheng Xingping Hu |
| author_facet | Weibo Wang Jin Yuan Bingrong Wang Yu Fang Yongkang Zheng Xingping Hu |
| author_sort | Weibo Wang |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-10T23:43:32Z |
| format | Article |
| id | doaj.art-51536d425a68447e8de408bb368f68f1 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-10T23:43:32Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-51536d425a68447e8de408bb368f68f12023-01-11T05:31:58ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-011712e027626410.1371/journal.pone.0276264Robust classification of heart valve sound based on adaptive EMD and feature fusion.Weibo WangJin YuanBingrong WangYu FangYongkang ZhengXingping HuCardiovascular 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.https://doi.org/10.1371/journal.pone.0276264 |
| spellingShingle | Weibo Wang Jin Yuan Bingrong Wang Yu Fang Yongkang Zheng Xingping Hu Robust classification of heart valve sound based on adaptive EMD and feature fusion. PLoS ONE |
| 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 |
| url | https://doi.org/10.1371/journal.pone.0276264 |
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