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

Xiaoling Wei, Jimin Li, Chenghao Zhang, Ming Liu, Peng Xiong, Xin Yuan, Yifei Li, Feng Lin and Xiuling Liu

Journal of Probability and Statistics, 2019, vol. 2019, 1-9

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.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnljps:8057820

DOI: 10.1155/2019/8057820

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