HE Hong, TAN Yonghong. A Novel Adaptive Wavelet Thresholding with Identical Correlation Shrinkage Function for ECG Noise Removal[J]. Chinese Journal of Electronics, 2018, 27(3): 507-513. doi: 10.1049/cje.2018.02.006
Citation: HE Hong, TAN Yonghong. A Novel Adaptive Wavelet Thresholding with Identical Correlation Shrinkage Function for ECG Noise Removal[J]. Chinese Journal of Electronics, 2018, 27(3): 507-513. doi: 10.1049/cje.2018.02.006

A Novel Adaptive Wavelet Thresholding with Identical Correlation Shrinkage Function for ECG Noise Removal

doi: 10.1049/cje.2018.02.006
Funds:  This work is supported by the National Natural Science Foundation of China (No.61571302, No.61371145, No.61671303), Industry-education-research Project of Shanghai Normal University (No.DCL201704), and the Project of the Science and Technology Commission of Shanghai (No.18070503000).
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  • Corresponding author: TAN Yonghong (corresponding author) received Ph.D. degree in electrical engineering from University of Ghent, Ghent, Belgium, in 1996. He is currently a professor at the College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai, China. His research interests include modeling and control of nonlinear systems, mechatronics, intelligent control, and signal processing. (Email:tany@shnu.edu.cn)
  • Received Date: 2017-05-17
  • Rev Recd Date: 2017-07-26
  • Publish Date: 2018-05-10
  • On the basis of wavelet theory, a novel Adaptive wavelet thresholding method (AWT) is proposed for the ECG signal enhancement. The best base wavelet for ECG signal filtering can be automatically obtained through the cross correlation coefficient and the energy to entropy ratio. The variable universal threshold (VarUniversal) is applied to different decomposition level so as to suppress diverse noise. To achieve a smooth cut-off transition, an identical correlation shrinkage function (IcoShrinkage) is also adopted in the AWT according to its correlation coefficients with the hard thresholding and the soft thresholding. The performance of AWT is compared with four threshold approaches and six shrinkage functions, respectively, on the basis of 150 practical ECG signals of 30 subjects. The filtering results reveal that the AWT can adaptively choose an optimal base wavelet for a specific ECG signal. With the VarUniversal threshold and IcoShrinkage, the AWT obtains the better filtering results than the other compared methods.
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