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Journal of Probability and Statistics
Volume 2019, Article ID 8057820, 9 pages
https://doi.org/10.1155/2019/8057820
Research Article

Atrial Fibrillation Detection by the Combination of Recurrence Complex Network and Convolution Neural Network

1Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding, China
2College of Cyber Security and Computer, Hebei University, Baoding, China
3Department of Applied Mathematics, School of Natural and Applied Sciences, Northwestern Polytechnical University, Xi’an, China
4Nanyang Technological University, Singapore

Correspondence should be addressed to Jimin Li; moc.qq@3994276201, Ming Liu; moc.621@reelg, Peng Xiong; moc.361@gnaixuoy.edgnoix, and Xiuling Liu; moc.liamtoh@121gniluixuil

Received 3 July 2018; Accepted 7 November 2018; Published 3 January 2019

Guest Editor: Min Zhang

Copyright © 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.

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.