Research on ECG de-noising method based on ensemble empirical mode decomposition and wavelet transform using improved threshold function.
- Author:
Linlin YE
;
Dan YANG
;
Xu WANG
- Publication Type:Journal Article
- MeSH:
Algorithms;
Electrocardiography;
Humans;
Signal Processing, Computer-Assisted;
Wavelet Analysis
- From:
Journal of Biomedical Engineering
2014;31(3):567-571
- CountryChina
- Language:Chinese
-
Abstract:
A de-noising method for electrocardiogram (ECG) based on ensemble empirical mode decomposition (EEMD) and wavelet threshold de-noising theory is proposed in our school. We decomposed noised ECG signals with the proposed method using the EEMD and calculated a series of intrinsic mode functions (IMFs). Then we selected IMFs and reconstructed them to realize the de-noising for ECG. The processed ECG signals were filtered again with wavelet transform using improved threshold function. In the experiments, MIT-BIH ECG database was used for evaluating the performance of the proposed method, contrasting with de-noising method based on EEMD and wavelet transform with improved threshold function alone in parameters of signal to noise ratio (SNR) and mean square error (MSE). The results showed that the ECG waveforms de-noised with the proposed method were smooth and the amplitudes of ECG features did not attenuate. In conclusion, the method discussed in this paper can realize the ECG denoising and meanwhile keep the characteristics of original ECG signal.
