Please use this identifier to cite or link to this item:
Title: Atrial fibrillation detection by the combination of recurrence complex network and convolution neural network
Authors: Wei, Xiaoling
Li, Jimin
Zhang, Chenghao
Liu, Ming
Xiong, Peng
Yuan, Xin
Li, Yifei
Lin, Feng
Liu, Xiuling
Keywords: Atrial Fibrillation Detection
DRNTU::Engineering::Computer science and engineering
Neural Network
Issue Date: 2019
Source: Wei, X., Li, J., Zhang, C., Liu, M., Xiong, P., Yuan, X., . . . Liu, X. (2019). Atrial Fibrillation Detection by the Combination of Recurrence Complex Network and Convolution Neural Network. Journal of Probability and Statistics, 2019, 8057820-. doi:10.1155/2019/8057820
Series/Report no.: Journal of Probability and Statistics
Abstract: 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.
ISSN: 1687-952X
DOI: 10.1155/2019/8057820
Rights: © 2019 Xiaoling Wei et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

Citations 20

Updated on Sep 2, 2020

Citations 20

Updated on Mar 4, 2021

Page view(s)

Updated on Jun 15, 2021

Download(s) 50

Updated on Jun 15, 2021

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.