Atrial Fibrillation Detection on Low-Power Wearables using Knowledge Distillation

Author(s):  
Antonino Faraone ◽  
Halla Sigurthorsdottir ◽  
Ricard Delgado-Gonzalo
2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Xiaoling Wei ◽  
Jimin Li ◽  
Chenghao Zhang ◽  
Ming Liu ◽  
Peng Xiong ◽  
...  

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.


2018 ◽  
Vol 251 ◽  
pp. 45-50 ◽  
Author(s):  
Jorge Pagola ◽  
Jesus Juega ◽  
Jaume Francisco-Pascual ◽  
Angel Moya ◽  
Mireia Sanchis ◽  
...  

Author(s):  
Xiangyu Zhang ◽  
Jianqing Li ◽  
Zhipeng Cai ◽  
Li Zhang ◽  
Zhenghua Chen ◽  
...  

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 53566-53575 ◽  
Author(s):  
Zhenning Mei ◽  
Xiao Gu ◽  
Hongyu Chen ◽  
Wei Chen

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