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

Abstract EN

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.