ECG Noise Cancellation Based on Grey Spectral Noise Estimation

Abstract

In recent years, wearable devices have been popularly applied in the health care field. The electrocardiogram (ECG) is the most used signal. However, the ECG is measured under a body-motion condition, which is easily coupled with some noise, like as power line noise (PLn) and electromyogram (EMG). This paper presents a grey spectral noise cancellation (GSNC) scheme for electrocardiogram (ECG) signals where two-stage discrimination is employed with the empirical mode decomposition (EMD), the ensemble empirical mode decomposition (EEMD) and the grey spectral noise estimation (GSNE). In the first stage of the proposed GSNC scheme, the input ECG signal is decomposed by the EMD to obtain a set of intrinsic mode functions (IMFs). Then, the noise energies of IMFs are estimated by the GSNE. When an IMF is considered as noisy one, it is forwarded to the second stage for further check. In the second stage, the suspicious IMFs are reconstructed and decomposed by the EEMD. Then the IMFs are discriminated with a threshold. If the IMF is considered as noisy, it is discarded in the reconstruction process of the ECG signal. The proposed GSNC scheme is justified by forty-three ECG signal datasets from the MIT-BIH cardiac arrhythmia database where the PLn and EMG noise are under consideration. The results indicate that the proposed GSNC scheme outperforms the traditional EMD and EEMD based noise cancellation schemes in the given datasets.

Computational Diagnostic Techniques for Electrocardiogram Signal Analysis

“…There are many EMD or EEMD-based approaches to ECG denoising generally deal with the PLI, EMG and white noise [38,39], and most of them mainly consider the noise energy in the first order IMF, which will occur the over-cancellation and eventually lead to distortion in the reconstructed ECG signal. Liu et al proposed a grey spectral noise cancellation (GSNC) scheme using EMD, EEMD and grey spectral noise estimation (GSNE) to deal with the PLI and EMG noises for ECG signals [40]. The proposed GSNC scheme is a two-stage discrimination scheme based on the IMFs' noise magnitude spectrum.…”

Noise-Resistant CECG Using Novel Capacitive Electrodes

“…As well known, raw CECG signals can be easily overridden or even damaged by a wide variety of noise sources, e.g., power lines [14][15][16], motion artifact [17,18], ambience changes, etc. Though ambience change is actually a common noise source in reality, there have been a limited number of publications on this issue.…”

Sparse ECG Denoising with Generalized Minimax Concave Penalty

“…Due to the ability of dealing with non-stationary and nonlinear signals, the EMD-based ECG denoising methods gained extensive attention recently [15,16,17,18,19,20,21]. The EMD method was first introduced in [22] for analyzing data from nonstationary and nonlinear processes.…”

“…Because of the mode mixing problem, it is difficult to separate the signal and noise components clearly even in higher order IMFs. To overcome this problem, paper [21] proposed a grey spectral noise cancellation (GSNC) scheme for ECG signals where two-stage discrimination is employed with the empirical mode decomposition (EMD), the ensemble empirical mode decomposition (EEMD) and the grey spectral noise estimation (GSNE). This method effectively alleviates the mode mixing problem.…”