Enhanced CAD Detection Using Novel Multi-Modal Learning: Integration of ECG, PCG, and Coupling Signals
Early and highly precise detection is essential for delaying the progression of coronary artery disease (CAD). Previous methods primarily based on single-modal data inherently lack sufficient information that compromises detection precision. This paper proposes a novel multi-modal learning method ai...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-10-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/11/11/1093 |
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| author | Chengfa Sun Xiaolei Liu Changchun Liu Xinpei Wang Yuanyuan Liu Shilong Zhao Ming Zhang |
| author_facet | Chengfa Sun Xiaolei Liu Changchun Liu Xinpei Wang Yuanyuan Liu Shilong Zhao Ming Zhang |
| author_sort | Chengfa Sun |
| collection | DOAJ |
| description | Early and highly precise detection is essential for delaying the progression of coronary artery disease (CAD). Previous methods primarily based on single-modal data inherently lack sufficient information that compromises detection precision. This paper proposes a novel multi-modal learning method aimed to enhance CAD detection by integrating ECG, PCG, and coupling signals. A novel coupling signal is initially generated by operating the deconvolution of ECG and PCG. Then, various entropy features are extracted from ECG, PCG, and its coupling signals, as well as recurrence deep features also encoded by integrating recurrence plots and a parallel-input 2-D CNN. After feature reduction and selection, final classification is performed by combining optimal multi-modal features and support vector machine. This method was validated on simultaneously recorded standard lead-II ECG and PCG signals from 199 subjects. The experimental results demonstrate that the proposed multi-modal method by integrating all signals achieved a notable enhancement in detection performance with best accuracy of 95.96%, notably outperforming results of single-modal and joint analysis with accuracies of 80.41%, 86.51%, 91.44%, and 90.42% using ECG, PCG, coupling signal, and joint ECG and PCG, respectively. This indicates that our multi-modal method provides more sufficient information for CAD detection, with the coupling information playing an important role in classification. |
| format | Article |
| id | doaj-art-9aaf02389e2f4bf5b006e3ab2d7af83a |
| institution | Kabale University |
| issn | 2306-5354 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj-art-9aaf02389e2f4bf5b006e3ab2d7af83a2024-11-26T17:51:54ZengMDPI AGBioengineering2306-53542024-10-011111109310.3390/bioengineering11111093Enhanced CAD Detection Using Novel Multi-Modal Learning: Integration of ECG, PCG, and Coupling SignalsChengfa Sun0Xiaolei Liu1Changchun Liu2Xinpei Wang3Yuanyuan Liu4Shilong Zhao5Ming Zhang6Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan 250061, ChinaDepartment of Electrical Automation Technology, Yantai Vocational College, Yantai 264670, ChinaDepartment of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan 250061, ChinaDepartment of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan 250061, ChinaDepartment of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan 250061, ChinaDepartment of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan 250061, ChinaHuiyironggong Technology Co., Ltd., Jinan 250098, ChinaEarly and highly precise detection is essential for delaying the progression of coronary artery disease (CAD). Previous methods primarily based on single-modal data inherently lack sufficient information that compromises detection precision. This paper proposes a novel multi-modal learning method aimed to enhance CAD detection by integrating ECG, PCG, and coupling signals. A novel coupling signal is initially generated by operating the deconvolution of ECG and PCG. Then, various entropy features are extracted from ECG, PCG, and its coupling signals, as well as recurrence deep features also encoded by integrating recurrence plots and a parallel-input 2-D CNN. After feature reduction and selection, final classification is performed by combining optimal multi-modal features and support vector machine. This method was validated on simultaneously recorded standard lead-II ECG and PCG signals from 199 subjects. The experimental results demonstrate that the proposed multi-modal method by integrating all signals achieved a notable enhancement in detection performance with best accuracy of 95.96%, notably outperforming results of single-modal and joint analysis with accuracies of 80.41%, 86.51%, 91.44%, and 90.42% using ECG, PCG, coupling signal, and joint ECG and PCG, respectively. This indicates that our multi-modal method provides more sufficient information for CAD detection, with the coupling information playing an important role in classification.https://www.mdpi.com/2306-5354/11/11/1093CADcoupling informationentropyrecurrence plotCNNfeature selection |
| spellingShingle | Chengfa Sun Xiaolei Liu Changchun Liu Xinpei Wang Yuanyuan Liu Shilong Zhao Ming Zhang Enhanced CAD Detection Using Novel Multi-Modal Learning: Integration of ECG, PCG, and Coupling Signals Bioengineering CAD coupling information entropy recurrence plot CNN feature selection |
| title | Enhanced CAD Detection Using Novel Multi-Modal Learning: Integration of ECG, PCG, and Coupling Signals |
| title_full | Enhanced CAD Detection Using Novel Multi-Modal Learning: Integration of ECG, PCG, and Coupling Signals |
| title_fullStr | Enhanced CAD Detection Using Novel Multi-Modal Learning: Integration of ECG, PCG, and Coupling Signals |
| title_full_unstemmed | Enhanced CAD Detection Using Novel Multi-Modal Learning: Integration of ECG, PCG, and Coupling Signals |
| title_short | Enhanced CAD Detection Using Novel Multi-Modal Learning: Integration of ECG, PCG, and Coupling Signals |
| title_sort | enhanced cad detection using novel multi modal learning integration of ecg pcg and coupling signals |
| topic | CAD coupling information entropy recurrence plot CNN feature selection |
| url | https://www.mdpi.com/2306-5354/11/11/1093 |
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