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

Author

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

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.

Suggested Citation

  • Xiaoling Wei & Jimin Li & Chenghao Zhang & Ming Liu & Peng Xiong & Xin Yuan & Yifei Li & Feng Lin & Xiuling Liu, 2019. "Atrial Fibrillation Detection by the Combination of Recurrence Complex Network and Convolution Neural Network," Journal of Probability and Statistics, Hindawi, vol. 2019, pages 1-9, January.
    • Handle: RePEc:hin:jnljps:8057820
    DOI: 10.1155/2019/8057820
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