Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (5): 147-152.DOI: 10.3778/j.issn.1002-8331.1811-0278

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ECG Signal Quality Analysis Based on Convolutional Neural Network

ZHANG Haibin, LIU Juan, LIU Sixuan, CHENG Yu   

  1. School of Computer Science, Wuhan University, Wuhan 430072, China
  • Online:2020-03-01 Published:2020-03-06



  1. 武汉大学 计算机学院,武汉 430072


At present, some rule-based or machine-based learning methods have been proposed to automatically classify the input Electrocardiogram(ECG) signal as clinically acceptable or unacceptable, and achieved good results. In this paper, an ECG signal quality assessment method based on convolution neural network is proposed, in which the features can be automatically learned by network model. Moreover, the strategy that evaluating fragment by fragment is adopted, which is different with other methods to evaluate the quality from a global perspective. In order to compare with other methods, local evaluating results together to get the overall quality assessment of ECGs are combined, the test results on public data show the good performance of the method.

Key words: electrocardiogram, signal quality analysis, convolution neural network



关键词: 心电图, 信号质量评估, 卷积神经网络