Classification of Current Density Vector Maps for Heart Failures Using a Transfer Convolutional Neural Network
Ischemic heart disease (IHD) is the leading cause of death worldwide. Magnetocardiogram (MCG) as a non-invasive detection of the heart, takes a more important role in clinic detection. However, the MCG technique is not a common diagnostic tool in routine clinical practice because of the lack of MCG...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9839426/ |
| _version_ | 1798039947018502144 |
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| author | Zhenghui Hu Yutong Lin Kaikai Ye Qiang Lin |
| author_facet | Zhenghui Hu Yutong Lin Kaikai Ye Qiang Lin |
| author_sort | Zhenghui Hu |
| collection | DOAJ |
| description | Ischemic heart disease (IHD) is the leading cause of death worldwide. Magnetocardiogram (MCG) as a non-invasive detection of the heart, takes a more important role in clinic detection. However, the MCG technique is not a common diagnostic tool in routine clinical practice because of the lack of MCG data and trained doctors for MCG data, especially for current density vector map (CDVM). Therefore, we propose an automatic method to analyze MCG data using the deep learning method. Here, we propose a deep learning method called Residual Network (ResNet) with transfer learning to classify CDVM from category 0 to category 4, which is reconstructed from MCG data. The ResNet exhibited an accuracy of 90.02%. This paper suggests a high potential for applying ResNet to CDVMs. |
| first_indexed | 2024-04-11T22:00:43Z |
| format | Article |
| id | doaj.art-cd05094ff5504532a507894d8710a0fe |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T22:00:43Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-cd05094ff5504532a507894d8710a0fe2022-12-22T04:00:57ZengIEEEIEEE Access2169-35362022-01-0110827668277510.1109/ACCESS.2022.31937699839426Classification of Current Density Vector Maps for Heart Failures Using a Transfer Convolutional Neural NetworkZhenghui Hu0https://orcid.org/0000-0002-3578-6192Yutong Lin1https://orcid.org/0000-0001-8038-4064Kaikai Ye2https://orcid.org/0000-0002-4474-3231Qiang Lin3https://orcid.org/0000-0001-9111-609XCollege of Science, Zhejiang University of Technology, Hangzhou, ChinaCollege of Science, Zhejiang University of Technology, Hangzhou, ChinaCollege of Science, Zhejiang University of Technology, Hangzhou, ChinaCollege of Science, Zhejiang University of Technology, Hangzhou, ChinaIschemic heart disease (IHD) is the leading cause of death worldwide. Magnetocardiogram (MCG) as a non-invasive detection of the heart, takes a more important role in clinic detection. However, the MCG technique is not a common diagnostic tool in routine clinical practice because of the lack of MCG data and trained doctors for MCG data, especially for current density vector map (CDVM). Therefore, we propose an automatic method to analyze MCG data using the deep learning method. Here, we propose a deep learning method called Residual Network (ResNet) with transfer learning to classify CDVM from category 0 to category 4, which is reconstructed from MCG data. The ResNet exhibited an accuracy of 90.02%. This paper suggests a high potential for applying ResNet to CDVMs.https://ieeexplore.ieee.org/document/9839426/Current density vector map (CDVM)magnetocardiogram (MCG)residual network (ResNet)transfer learning |
| spellingShingle | Zhenghui Hu Yutong Lin Kaikai Ye Qiang Lin Classification of Current Density Vector Maps for Heart Failures Using a Transfer Convolutional Neural Network IEEE Access Current density vector map (CDVM) magnetocardiogram (MCG) residual network (ResNet) transfer learning |
| title | Classification of Current Density Vector Maps for Heart Failures Using a Transfer Convolutional Neural Network |
| title_full | Classification of Current Density Vector Maps for Heart Failures Using a Transfer Convolutional Neural Network |
| title_fullStr | Classification of Current Density Vector Maps for Heart Failures Using a Transfer Convolutional Neural Network |
| title_full_unstemmed | Classification of Current Density Vector Maps for Heart Failures Using a Transfer Convolutional Neural Network |
| title_short | Classification of Current Density Vector Maps for Heart Failures Using a Transfer Convolutional Neural Network |
| title_sort | classification of current density vector maps for heart failures using a transfer convolutional neural network |
| topic | Current density vector map (CDVM) magnetocardiogram (MCG) residual network (ResNet) transfer learning |
| url | https://ieeexplore.ieee.org/document/9839426/ |
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