Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (16): 148-155.DOI: 10.3778/j.issn.1002-8331.1906-0089

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ECG Signal Denoising Based on Convolutional Auto-encoder Neural Network

CHEN Jian, LIU Ming, XIONG Peng, MENG Xianhui, YANG Lin   

  1. Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic Information Engineering, Hebei University, Baoding, Hebei 071002, China
  • Online:2020-08-15 Published:2020-08-11



  1. 河北大学 电子信息工程学院,河北省数字医疗工程重点实验室,河北 保定 071002


The ECG signal is seriously submerged due to interference from the external environment during the acquisition process, thus causing interference to the doctor’s diagnosis and remote intelligent analysis. Based on this, this paper proposes an ECG signal denoising algorithm based on convolutional auto-encoder neural network. The method utilizes the encoding and decoding characteristics of the auto-encoder, and constructs a deep neural network by convolution to learn the end-to-end mapping from the noisy ECG signal to the clean ECG signal. The convolution layer captures the details of ECG signals content while eliminating noise; the decoding portion can upsample the feature map and restore the ECG signal details to obtain a clean ECG signal. In the experiment, the signal-to-noise ratio and root mean square error are used as indicators, and the method is compared with wavelet threshold method, S-transform method, BP neural network method and guided filtering method. The experimental results show that the overall noise reduction accuracy of the noise reduction method is better, and the low frequency components of the signal are also well preserved. The method can eliminate the complex noise in the ECG signal while completely retaining the shape of the ECG signal, which lays a foundation for the intelligent diagnosis of cardiovascular disease and the characteristic detection of ECG.

Key words: ECG signal, denoising, auto-encoder, convolutional neural network



关键词: 心电信号, 降噪, 自编码器, 卷积神经网络