Atrial fibrillation detection by the combination of recurrence complex network and convolution neural network

• Main Authors: Wei, Xiaoling, Li, Jimin, Zhang, Chenghao, Liu, Ming, Xiong, Peng, Yuan, Xin, Li, Yifei, Lin, Feng, Liu, Xiuling
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2019
• Subjects: Atrial Fibrillation Detection; DRNTU::Engineering::Computer science and engineering; Neural Network
• Online Access: https://hdl.handle.net/10356/90259; http://hdl.handle.net/10220/48455
• Institution: Nanyang Technological University

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.