Atrial Fibrillation Detection by the Combination of Recurrence Complex Network and Convolution Neural Network. (3rd January 2019)

• Record Type: Journal Article
• Authors: Wei, Xiaoling; Li, Jimin; Zhang, Chenghao; Liu, Ming; Xiong, Peng; Yuan, Xin; Li, Yifei; Lin, Feng; Liu, Xiuling
• Other Names: Zhang Min Guest Editor.
• Is Part Of: Journal of probability and statistics. Volume 2019(2019)
• Publication Date: 2019-01-03
• Subjects: Probabilities -- Periodicals; Mathematical statistics -- Periodicals; Mathematical statistics; Probabilities; Periodicals; 519
• DOI: 10.1155/2019/8057820
• Languages: English
• ISSNs: 1687-952X

Abstract

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.