Machine learning-aided detection of heart failure (LVEF ≤ 49%) by using ballistocardiography and respiratory effort signals
Purpose: Under the influence of COVID-19 and the in-hospital cost, the in-home detection of cardiovascular disease with smart sensing devices is becoming more popular recently. In the presence of the qualified signals, ballistocardiography (BCG) can not only reflect the cardiac mechanical movements,...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-01-01
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| Series: | Frontiers in Physiology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fphys.2022.1068824/full |
| _version_ | 1797948200205680640 |
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| author | Shen Feng Shen Feng Xianda Wu Xianda Wu Andong Bao Andong Bao Guanyang Lin Guanyang Lin Pengtao Sun Huan Cen Sinan Chen Yuexia Liu Wenning He Zhiqiang Pang Han Zhang Han Zhang |
| author_facet | Shen Feng Shen Feng Xianda Wu Xianda Wu Andong Bao Andong Bao Guanyang Lin Guanyang Lin Pengtao Sun Huan Cen Sinan Chen Yuexia Liu Wenning He Zhiqiang Pang Han Zhang Han Zhang |
| author_sort | Shen Feng |
| collection | DOAJ |
| description | Purpose: Under the influence of COVID-19 and the in-hospital cost, the in-home detection of cardiovascular disease with smart sensing devices is becoming more popular recently. In the presence of the qualified signals, ballistocardiography (BCG) can not only reflect the cardiac mechanical movements, but also detect the HF in a non-contact manner. However, for the potential HF patients, the additional quality assessment with ECG-aided requires more procedures and brings the inconvenience to their in-home HF diagnosis. To enable the HF detection in many real applications, we proposed a machine learning-aided scheme for the HF detection in this paper, where the BCG signals recorded from the force sensor were employed without the heartbeat location, and the respiratory effort signals separated from force sensors provided more HF features due to the connection between the heart and the lung systems. Finally, the effectiveness of the proposed HF detection scheme was verified in comparative experiments.Methods: First, a piezoelectric sensor was used to record a signal sequences of the two-dimensional vital sign, which includes the BCG and the respiratory effort. Then, the linear and the non-linear features w.r.t. BCG and respiratory effort signals were extracted to serve the HF detection. Finally, the improved HF detection performance was verified through the LOO and the LOSO cross-validation settings with different machine learning classifiers.Results: The proposed machine learning-aided scheme achieved the robust performance in the HF detection by using 4 different classifiers, and yielded an accuracy of 94.97% and 87.00% in the LOO and the LOSO experiments, respectively. In addition, experimental results demonstrated that the designed respiratory and cardiopulmonary features are beneficial to the HF detection (LVEF ≤49%).Conclusion: This study proposed a machine learning-aided HF diagnostic scheme. Experimental results demonstrated that the proposed scheme can fully exploit the relationship between the heart and the lung systems to potentially improve the in-home HF detection performance by using both the BCG, the respiratory and the cardiopulmonary-related features. |
| first_indexed | 2024-04-10T21:40:43Z |
| format | Article |
| id | doaj.art-c831ffce94ca463db58d69fcd2f7ef4f |
| institution | Directory Open Access Journal |
| issn | 1664-042X |
| language | English |
| last_indexed | 2024-04-10T21:40:43Z |
| publishDate | 2023-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physiology |
| spelling | doaj.art-c831ffce94ca463db58d69fcd2f7ef4f2023-01-19T05:55:37ZengFrontiers Media S.A.Frontiers in Physiology1664-042X2023-01-011310.3389/fphys.2022.10688241068824Machine learning-aided detection of heart failure (LVEF ≤ 49%) by using ballistocardiography and respiratory effort signalsShen Feng0Shen Feng1Xianda Wu2Xianda Wu3Andong Bao4Andong Bao5Guanyang Lin6Guanyang Lin7Pengtao Sun8Huan Cen9Sinan Chen10Yuexia Liu11Wenning He12Zhiqiang Pang13Han Zhang14Han Zhang15Department of Electronics and Information Engineering, South China Normal University (SCNU), Foshan, ChinaSchool of Physics and Telecommunication Engineering, South China Normal University (SCNU), Guangzhou, ChinaDepartment of Electronics and Information Engineering, South China Normal University (SCNU), Foshan, ChinaSchool of Physics and Telecommunication Engineering, South China Normal University (SCNU), Guangzhou, ChinaDepartment of Electronics and Information Engineering, South China Normal University (SCNU), Foshan, ChinaSchool of Physics and Telecommunication Engineering, South China Normal University (SCNU), Guangzhou, ChinaDepartment of Electronics and Information Engineering, South China Normal University (SCNU), Foshan, ChinaSchool of Physics and Telecommunication Engineering, South China Normal University (SCNU), Guangzhou, ChinaDepartment of Ultrasonography, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, ChinaDepartment of Ultrasonography, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, ChinaDepartment of Ultrasonography, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, ChinaDepartment of Ultrasonography, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, ChinaGuangzhou SENVIV Technology Co., Ltd., Guangzhou, ChinaGuangzhou SENVIV Technology Co., Ltd., Guangzhou, ChinaDepartment of Electronics and Information Engineering, South China Normal University (SCNU), Foshan, ChinaSchool of Physics and Telecommunication Engineering, South China Normal University (SCNU), Guangzhou, ChinaPurpose: Under the influence of COVID-19 and the in-hospital cost, the in-home detection of cardiovascular disease with smart sensing devices is becoming more popular recently. In the presence of the qualified signals, ballistocardiography (BCG) can not only reflect the cardiac mechanical movements, but also detect the HF in a non-contact manner. However, for the potential HF patients, the additional quality assessment with ECG-aided requires more procedures and brings the inconvenience to their in-home HF diagnosis. To enable the HF detection in many real applications, we proposed a machine learning-aided scheme for the HF detection in this paper, where the BCG signals recorded from the force sensor were employed without the heartbeat location, and the respiratory effort signals separated from force sensors provided more HF features due to the connection between the heart and the lung systems. Finally, the effectiveness of the proposed HF detection scheme was verified in comparative experiments.Methods: First, a piezoelectric sensor was used to record a signal sequences of the two-dimensional vital sign, which includes the BCG and the respiratory effort. Then, the linear and the non-linear features w.r.t. BCG and respiratory effort signals were extracted to serve the HF detection. Finally, the improved HF detection performance was verified through the LOO and the LOSO cross-validation settings with different machine learning classifiers.Results: The proposed machine learning-aided scheme achieved the robust performance in the HF detection by using 4 different classifiers, and yielded an accuracy of 94.97% and 87.00% in the LOO and the LOSO experiments, respectively. In addition, experimental results demonstrated that the designed respiratory and cardiopulmonary features are beneficial to the HF detection (LVEF ≤49%).Conclusion: This study proposed a machine learning-aided HF diagnostic scheme. Experimental results demonstrated that the proposed scheme can fully exploit the relationship between the heart and the lung systems to potentially improve the in-home HF detection performance by using both the BCG, the respiratory and the cardiopulmonary-related features.https://www.frontiersin.org/articles/10.3389/fphys.2022.1068824/fullheart failureballistocardiographyrespiratoryclassifierhome monitoring |
| spellingShingle | Shen Feng Shen Feng Xianda Wu Xianda Wu Andong Bao Andong Bao Guanyang Lin Guanyang Lin Pengtao Sun Huan Cen Sinan Chen Yuexia Liu Wenning He Zhiqiang Pang Han Zhang Han Zhang Machine learning-aided detection of heart failure (LVEF ≤ 49%) by using ballistocardiography and respiratory effort signals Frontiers in Physiology heart failure ballistocardiography respiratory classifier home monitoring |
| title | Machine learning-aided detection of heart failure (LVEF ≤ 49%) by using ballistocardiography and respiratory effort signals |
| title_full | Machine learning-aided detection of heart failure (LVEF ≤ 49%) by using ballistocardiography and respiratory effort signals |
| title_fullStr | Machine learning-aided detection of heart failure (LVEF ≤ 49%) by using ballistocardiography and respiratory effort signals |
| title_full_unstemmed | Machine learning-aided detection of heart failure (LVEF ≤ 49%) by using ballistocardiography and respiratory effort signals |
| title_short | Machine learning-aided detection of heart failure (LVEF ≤ 49%) by using ballistocardiography and respiratory effort signals |
| title_sort | machine learning aided detection of heart failure lvef ≤ 49 by using ballistocardiography and respiratory effort signals |
| topic | heart failure ballistocardiography respiratory classifier home monitoring |
| url | https://www.frontiersin.org/articles/10.3389/fphys.2022.1068824/full |
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