Atrial Fibrillation Detection by the Combination of Recurrence Complex Network and Convolution Neural Network
In this paper, R wave peak interval independent atrial fibrillation detection algorithm is proposed based on the analysis of the synchronization feature of the electrocardiogram signal by a deep neural network. Firstly, the synchronization feature of each heartbeat of the electrocardiogram signal is...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-01-01
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| Series: | Journal of Probability and Statistics |
| Online Access: | http://dx.doi.org/10.1155/2019/8057820 |
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| _version_ | 1832545776425762816 |
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| author | Xiaoling Wei Jimin Li Chenghao Zhang Ming Liu Peng Xiong Xin Yuan Yifei Li Feng Lin Xiuling Liu |
| author_facet | Xiaoling Wei Jimin Li Chenghao Zhang Ming Liu Peng Xiong Xin Yuan Yifei Li Feng Lin Xiuling Liu |
| author_sort | Xiaoling Wei |
| collection | DOAJ |
| description | In this paper, R wave peak interval independent atrial fibrillation detection algorithm is proposed based on the analysis of the synchronization feature of the electrocardiogram signal by a deep neural network. Firstly, the synchronization feature of each heartbeat of the electrocardiogram signal is constructed by a Recurrence Complex Network. Then, a convolution neural network is used to detect atrial fibrillation by analyzing the eigenvalues of the Recurrence Complex Network. Finally, a voting algorithm is developed to improve the performance of the beat-wise atrial fibrillation detection. The MIT-BIH atrial fibrillation database is used to evaluate the performance of the proposed method. Experimental results show that the sensitivity, specificity, and accuracy of the algorithm can achieve 94.28%, 94.91%, and 94.59%, respectively. Remarkably, the proposed method was more effective than the traditional algorithms to the problem of individual variation in the atrial fibrillation detection. |
| format | Article |
| id | doaj-art-43340aa6ac2c4886a7b95f5e85fb2045 |
| institution | Kabale University |
| issn | 1687-952X 1687-9538 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Probability and Statistics |
| spelling | doaj-art-43340aa6ac2c4886a7b95f5e85fb20452025-02-03T07:24:46ZengWileyJournal of Probability and Statistics1687-952X1687-95382019-01-01201910.1155/2019/80578208057820Atrial Fibrillation Detection by the Combination of Recurrence Complex Network and Convolution Neural NetworkXiaoling Wei0Jimin Li1Chenghao Zhang2Ming Liu3Peng Xiong4Xin Yuan5Yifei Li6Feng Lin7Xiuling Liu8Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding, ChinaCollege of Cyber Security and Computer, Hebei University, Baoding, ChinaDepartment of Applied Mathematics, School of Natural and Applied Sciences, Northwestern Polytechnical University, Xi’an, ChinaKey Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding, ChinaKey Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding, ChinaKey Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding, ChinaKey Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding, ChinaNanyang Technological University, SingaporeKey Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding, ChinaIn this paper, R wave peak interval independent atrial fibrillation detection algorithm is proposed based on the analysis of the synchronization feature of the electrocardiogram signal by a deep neural network. Firstly, the synchronization feature of each heartbeat of the electrocardiogram signal is constructed by a Recurrence Complex Network. Then, a convolution neural network is used to detect atrial fibrillation by analyzing the eigenvalues of the Recurrence Complex Network. Finally, a voting algorithm is developed to improve the performance of the beat-wise atrial fibrillation detection. The MIT-BIH atrial fibrillation database is used to evaluate the performance of the proposed method. Experimental results show that the sensitivity, specificity, and accuracy of the algorithm can achieve 94.28%, 94.91%, and 94.59%, respectively. Remarkably, the proposed method was more effective than the traditional algorithms to the problem of individual variation in the atrial fibrillation detection.http://dx.doi.org/10.1155/2019/8057820 |
| spellingShingle | Xiaoling Wei Jimin Li Chenghao Zhang Ming Liu Peng Xiong Xin Yuan Yifei Li Feng Lin Xiuling Liu Atrial Fibrillation Detection by the Combination of Recurrence Complex Network and Convolution Neural Network Journal of Probability and Statistics |
| title | Atrial Fibrillation Detection by the Combination of Recurrence Complex Network and Convolution Neural Network |
| title_full | Atrial Fibrillation Detection by the Combination of Recurrence Complex Network and Convolution Neural Network |
| title_fullStr | Atrial Fibrillation Detection by the Combination of Recurrence Complex Network and Convolution Neural Network |
| title_full_unstemmed | Atrial Fibrillation Detection by the Combination of Recurrence Complex Network and Convolution Neural Network |
| title_short | Atrial Fibrillation Detection by the Combination of Recurrence Complex Network and Convolution Neural Network |
| title_sort | atrial fibrillation detection by the combination of recurrence complex network and convolution neural network |
| url | http://dx.doi.org/10.1155/2019/8057820 |
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