Electronic Science and Technology ›› 2019, Vol. 32 ›› Issue (11): 7-11.doi: 10.16180/j.cnki.issn1007-7820.2019.11.002

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ECG Signal Classification Based on Filtering-Reconstruction and Convolutional Neural Network

WEI Zhangyuehao,QIAN Shengyi   

  1. School of Electronic Information,Hangzhou Dianzi University, Hangzhou 310018,China
  • Received:2018-11-06 Online:2019-11-15 Published:2019-11-15
  • Supported by:
    The Key Project Supported by Zhejiang Provincial Natural Science Foundation of China(LZ14F020002)


Automatic classification of ECG signals by computers can relieve work pressure of doctors and greatly improve diagnosis speed and accuracy. Aiming at the problem of complex feature extraction process and weak anti-interference ability in traditional algorithms, this paper proposed an ECG signal classification algorithm combined with filtering-reconstruction and convolutional neural networks. Firstly, the traditional signal filtering and heartbeat sequence reconstruction were used to remove the noise interference in the original ECG signal, and then the convolutional neural network was constructed to automatically learn the ECG signal feature and completed the classification. The results of the classification experiments on the PhysioNet/CinC Challenge 2017 dataset showed that this method had an average F1 (the average of the precision and recall ratio) of 0.8471, which was better than the methods based on artificial feature extraction and conventional convolutional network, and had strong anti-interference ability.

Key words: convolutional neural network, electrocardiogram signal, automatic feature extraction, sequence reconstruction, signal filtering, classification algorithm

CLC Number: 

  • TP391