ECG signal denoising and baseline wander correction based on the empirical mode decomposition

In non-parametric signal denoising area, empirical mode decomposition is potentially useful. In this paper, the wavelet thresholding principle is directly used in EMD-based denoising. The basic principle of the method is to reconstruct the signal with IMFs previously thresholded. A novel threshold function is proposed to improve denoising effect by exploiting the special characteristics of the hard and soft thresholding method. The denoising method is validated through experiments on the “Doppler” signal and a real ECG signal from MIT-BIH databases corrupted by additive white Gaussian random noise. The simulations show that the proposed EMD-based method provides very good results for denoising.

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Comparative Study of ECG Signal Denoising by Empirical Mode Decomposition and Thresholding Functions

2019 6th International Conference on Electrical and Electronics Engineering (ICEEE) ◽

10.1109/iceee2019.2019.00032 ◽

2019 ◽

Cited By ~ 1

Author(s):

Wahiba Mohguen ◽

Rais El'hadi Bekka

Keyword(s):

Comparative Study ◽

Empirical Mode Decomposition ◽

Signal Denoising ◽

Ecg Signal ◽

Mode Decomposition

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An Efficient ECG Denoising Method Based on Empirical Mode Decomposition, Sample Entropy, and Improved Threshold Function

Wireless Communications and Mobile Computing ◽

10.1155/2020/8811962 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Dengyong Zhang ◽

Shanshan Wang ◽

Feng Li ◽

Shang Tian ◽

Jin Wang ◽

...

Keyword(s):

Empirical Mode Decomposition ◽

Sample Entropy ◽

Threshold Function ◽

Signal Denoising ◽

Ecg Signal ◽

Intrinsic Mode Functions ◽

Denoising Method ◽

Ecg Signals ◽

Mode Decomposition ◽

Ecg Denoising

The electrocardiogram (ECG) signal can easily be affected by various types of noises while being recorded, which decreases the accuracy of subsequent diagnosis. Therefore, the efficient denoising of ECG signals has become an important research topic. In the paper, we proposed an efficient ECG denoising approach based on empirical mode decomposition (EMD), sample entropy, and improved threshold function. This method can better remove the noise of ECG signals and provide better diagnosis service for the computer-based automatic medical system. The proposed work includes three stages of analysis: (1) EMD is used to decompose the signal into finite intrinsic mode functions (IMFs), and according to the sample entropy of each order of IMF following EMD, the order of IMFs denoised is determined; (2) the new threshold function is adopted to denoise these IMFs after the order of IMFs denoised is determined; and (3) the signal is reconstructed and smoothed. The proposed method solves the shortcoming of discarding the first-order IMF directly in traditional EMD denoising and proposes a new threshold denoising function to improve the traditional soft and hard threshold functions. We further conduct simulation experiments of ECG signals from the MIT-BIH database, in which three types of noise are simulated: white Gaussian noise, electromyogram (EMG), and power line interference. The experimental results show that the proposed method is robust to a variety of noise types. Moreover, we analyze the effectiveness of the proposed method under different input SNR with reference to improving SNR ( SNR imp ) and mean square error ( MSE ), then compare the denoising algorithm proposed in this paper with previous ECG signal denoising techniques. The results demonstrate that the proposed method has a higher SNR imp and a lower MSE . Qualitative and quantitative studies demonstrate that the proposed algorithm is a good ECG signal denoising method.

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