Multi-classification method of arrhythmia based on multi-scale residual neural network and multi-channel data fusion
Since ECG contains key characteristic information of arrhythmias, extracting this information is crucial for identifying arrhythmias. Based on this, in order to effectively extract ECG data features and realize automatic detection of arrhythmia, a multi-classification method of arrhythmia based on m...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-09-01
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| Series: | Frontiers in Physiology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fphys.2023.1253907/full |
| _version_ | 1827804581592039424 |
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| author | Fuchun Zhang Meng Li Li Song Liang Wu Baiyang Wang |
| author_facet | Fuchun Zhang Meng Li Li Song Liang Wu Baiyang Wang |
| author_sort | Fuchun Zhang |
| collection | DOAJ |
| description | Since ECG contains key characteristic information of arrhythmias, extracting this information is crucial for identifying arrhythmias. Based on this, in order to effectively extract ECG data features and realize automatic detection of arrhythmia, a multi-classification method of arrhythmia based on multi-scale residual neural network and multi-channel data fusion is proposed. First, the features of single-lead ECG signals are extracted and converted into two-dimensional images, and the feature data sets are labeled and divided according to different types of arrhythmias. The improved residual neural network is trained on the training set to obtain the classification model of the neural network. Finally, the classification model is applied to the automatic detection of arrhythmias during exercise. The accuracy of the classification model of this method is as high as 99.60%, and it has high accuracy and generalization ability. The automatic identification of arrhythmia also contributes to the research and development of future wearable devices. |
| first_indexed | 2024-03-11T21:13:54Z |
| format | Article |
| id | doaj.art-7f843e0af7924967bd0c145bb9475802 |
| institution | Directory Open Access Journal |
| issn | 1664-042X |
| language | English |
| last_indexed | 2024-03-11T21:13:54Z |
| publishDate | 2023-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physiology |
| spelling | doaj.art-7f843e0af7924967bd0c145bb94758022023-09-29T05:42:00ZengFrontiers Media S.A.Frontiers in Physiology1664-042X2023-09-011410.3389/fphys.2023.12539071253907Multi-classification method of arrhythmia based on multi-scale residual neural network and multi-channel data fusionFuchun Zhang0Meng Li1Li Song2Liang Wu3Baiyang Wang4School of Information Science and Engineering, Linyi University, Linyi, ChinaSchool of Information Science and Engineering, Linyi University, Linyi, ChinaSchool of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai’an, ChinaSchool of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai’an, ChinaSchool of Information Science and Engineering, Shandong University, Qingdao, ChinaSince ECG contains key characteristic information of arrhythmias, extracting this information is crucial for identifying arrhythmias. Based on this, in order to effectively extract ECG data features and realize automatic detection of arrhythmia, a multi-classification method of arrhythmia based on multi-scale residual neural network and multi-channel data fusion is proposed. First, the features of single-lead ECG signals are extracted and converted into two-dimensional images, and the feature data sets are labeled and divided according to different types of arrhythmias. The improved residual neural network is trained on the training set to obtain the classification model of the neural network. Finally, the classification model is applied to the automatic detection of arrhythmias during exercise. The accuracy of the classification model of this method is as high as 99.60%, and it has high accuracy and generalization ability. The automatic identification of arrhythmia also contributes to the research and development of future wearable devices.https://www.frontiersin.org/articles/10.3389/fphys.2023.1253907/fulldeep learningmulti-scaleresidual neural networkselectrocardiogrammultichannel fusion |
| spellingShingle | Fuchun Zhang Meng Li Li Song Liang Wu Baiyang Wang Multi-classification method of arrhythmia based on multi-scale residual neural network and multi-channel data fusion Frontiers in Physiology deep learning multi-scale residual neural networks electrocardiogram multichannel fusion |
| title | Multi-classification method of arrhythmia based on multi-scale residual neural network and multi-channel data fusion |
| title_full | Multi-classification method of arrhythmia based on multi-scale residual neural network and multi-channel data fusion |
| title_fullStr | Multi-classification method of arrhythmia based on multi-scale residual neural network and multi-channel data fusion |
| title_full_unstemmed | Multi-classification method of arrhythmia based on multi-scale residual neural network and multi-channel data fusion |
| title_short | Multi-classification method of arrhythmia based on multi-scale residual neural network and multi-channel data fusion |
| title_sort | multi classification method of arrhythmia based on multi scale residual neural network and multi channel data fusion |
| topic | deep learning multi-scale residual neural networks electrocardiogram multichannel fusion |
| url | https://www.frontiersin.org/articles/10.3389/fphys.2023.1253907/full |
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