ECG Signal De-noising and Baseline Wander Correction Based on CEEMDAN and Wavelet Threshold.

Tao Li,Yang Xu,Mingzhang Luo,Gangbing Song

doi:10.3390/s17122754

Abstract

A novel electrocardiogram (ECG) signal de-noising and baseline wander correction method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and wavelet threshold is proposed. Although CEEMDAN is based on empirical mode decomposition (EMD), it represents a significant improvement of the original EMD by overcoming the mode-mixing problem. However, there has been no previous study on using CEEMDAN to de-noise ECG signals, to the authors’ best knowledge. In the proposed method, the original noisy ECG signal is decomposed into a series of intrinsic mode functions (IMFs) sorted from high to low frequency by CEEMDAN. Each IMF is then analyzed by the autocorrelation method to find out the first few high frequency IMFs containing random noise, and these IMFs should be de-noised by the wavelet threshold. The zero-crossing rate (ZCR) of all IMFs, including final residue, are computed, and the IMFs with ZCR less than a certain value are removed. Finally, the remaining IMFs are reconstructed to obtain the clean ECG signal. The proposed algorithm is validated through experiments using the MIT–BIH ECG databases, and the results show that the random noise in the ECG signal can be effectively suppressed, and at the same time the baseline wander can be corrected efficiently.

Highlights

The electrocardiogram (ECG) has been widely used for the clinical diagnosis of heart disease.ECG is a weak, non-linear and non-stationary human physiological signal
ECG databases, and the results show that the random noise in the ECG signal can be effectively suppressed, and at the same time the baseline wander can be corrected efficiently
After the remaining intrinsic mode functions (IMFs) are reconstructed, the clean ECG signal is obtained. This methodology is validated through experiments on the MIT–BIH ECG databases, and results show that the random noise in the ECG signal can be effectively suppressed and the baseline wander can be corrected efficiently as well

Summary

Introduction

The electrocardiogram (ECG) has been widely used for the clinical diagnosis of heart disease. The QT interval represents the time of ventricular activity, including both depolarization and repolarization It is measured from the beginning of the QRS complex to the end of the T wave [1]. The commonly used ECG signal de-noising methods include the morphological filtering method, the adaptive filtering method, the wavelet-based method, and the empirical mode decomposition (EMD) method. Proposed an EMD-based algorithm to remove the baseline wander and high-frequency noise of ECGs. Singh and Sunkaria [29] developed an ECG signal de-noising method based on the empirical mode decomposition and the moving average filter. Ye et al [34] studied the de-noising method of ECG signals based on the EEMD and improved the wavelet threshold. This methodology is validated through experiments on the MIT–BIH ECG databases, and results show that the random noise in the ECG signal can be effectively suppressed and the baseline wander can be corrected efficiently as well

Basic Principle

Improved Wavelet Threshold Function

Flowchart of ECG Signal De-Noising

Random Noise Suppression in the ECG Signal

ECG Baseline Wander Correction

Synthetic Noisy ECG Signal De-Noising Results

Real ECG Signal De-Noising Results