Gaussian Noise Filtering from ECG by Wiener Filter and Ensemble Empirical Mode Decomposition

Journal of Signal Processing Systems (Impact Factor: 0.6). 08/2011; 64(2):249-264. DOI: 10.1007/s11265-009-0447-z
Source: DBLP


Empirical mode decomposition (EMD) is a powerful algorithm that decomposes signals as a set of intrinsic mode function (IMF)
based on the signal complexity. In this study, partial reconstruction of IMF acting as a filter was used for noise reduction
in ECG. An improved algorithm, ensemble EMD (EEMD), was used for the first time to improve the noise-filtering performance,
based on the mode-mixing reduction between near IMF scales. Both standard ECG templates derived from simulator and Arrhythmia
ECG database were used as ECG signal, while Gaussian white noise was used as noise source. Mean square error (MSE) between
the reconstructed ECG and original ECG was used as the filter performance indicator. FIR Wiener filter was also used to compare
the filtering performance with EEMD. Experimental result showed that EEMD had better noise-filtering performance than EMD
and FIR Wiener filter. The average MSE ratios of EEMD to EMD and FIR Wiener filter were 0.71 and 0.61, respectively. Thus,
this study investigated an ECG noise-filtering procedure based on EEMD. Also, the optimal added noise power and trial number
for EEMD was also examined.

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Available from: Kang-Ming Chang, Feb 14, 2014
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    • "Conventional filters such as the finite impulse response (FIR) [1] [2], infinite impulse response (IIR) [3] [4], filter banks [5], polynomial filter [6] and Wiener filter [7] have been proposed in the literature to minimize artifacts. Other approaches for ECG denoising include adaptive filters, namely the least mean square (LMS), recursive least square (RLS) and their variants such as the block LMS (BLMS), normalized sign-sign LMS (NLMS) etc., [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20]. "
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    ABSTRACT: Electrocardiogram (ECG) is a widely used non-invasive method to study the rhythmic activity of the heart. These signals, however, are often obscured by artifacts/noises from various sources and minimization of these artifacts is of paramount importance for detecting anomalies. This paper presents a thorough analysis of the performance of two hybrid signal processing schemes ((i) Ensemble Empirical Mode Decomposition (EEMD) based method in conjunction with the Block Least Mean Square (BLMS) adaptive algorithm (EEMD-BLMS), and (ii) Discrete Wavelet Transform (DWT) combined with the Neural Network (NN), named the Wavelet NN (WNN)) for denoising the ECG signals. These methods are compared to the conventional EMD (C-EMD), C-EEMD, EEMD-LMS as well as the DWT thresholding (DWT-Th) based methods through extensive simulation studies on real as well as noise corrupted ECG signals. Results clearly show the superiority of the proposed methods.
    Full-text · Article · Mar 2016 · Biomedical Signal Processing and Control
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    • "Rectangles show regions that incorporate the majority of energy of the analyzed specific signal waves. The observation that EEMD provides better results for real-life signal processing in comparison to EMD completely agrees with the results presented in the other investigations: EMD decomposition spreads the energy among different IMFs in different time instances and it is unpredictable which IMFs incorporate energy of the analyzed specific signal waves [18] [29] [30] "
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    ABSTRACT: This paper presents an application of ensemble empirical mode decomposition method for enhancement of specific biological signal features. The application for two types of cardiological signals is presented in this article. Detection of fiducial points is a routine task for analyzing these signals. In a clinical situation, cardiological signals are usually corrupted by artifacts and finding exact time instances of various fiducial points is a challenge. Filtering approach for signal to noise ratio enhancing is traditionally and widely used in clinical practice. Methods, based on filtering, however, have serious limitations when it is necessary to find compromise between noise suppression and preservation of signal features. The proposed method uses ensemble empirical mode decomposition in order to suppress noise or enhance specific waves in the signal. Performance of the method was estimated by using clinical electrocardiogram and impedance cardiogram signals with synthetic baseline-wander, power-line and added Gaussian noise. In electrocardiogram application, an average estimation error of QRS complex length was 2.06-4.47%, the smallest in comparison to the reference methods. In impedance cardiogram application, the proposed method provided the highest cross-correlation coefficient between original and de-noised signal in comparison to reference methods. When the signal to noise ratio of the input signal was -12dB, the method provided signal to error ratio of 33dB in this case. The proposed method is adaptive to template and signal itself and thus could be applied to other non-stationary biological signals.
    Full-text · Article · Aug 2013 · Medical Engineering & Physics
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    • "As a more robust and noise-assisted version of EMD, EEMD can be used as an alternate in EMD based denoising methods. Furthermore, the use of the EEMD process as a filter and its comparisons with the EMD method have just recently been studied in [17] [18]. An improved filtering performance can be achieved by EEMD than EMD with suitable added noise and sufficient trial number. "
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    ABSTRACT: Ensemble empirical mode decomposition (EEMD) has been recently used to recover a signal from observed noisy data. Typically this is performed by partial reconstruction or thresholding operation. In this paper we describe an efficient noise reduction method. EEMD is used to decompose a signal into several intrinsic mode functions (IMFs). The time intervals between two adjacent zero-crossings within the IMF, called instantaneous half period (IHP), are used as a criterion to detect and classify the noise oscillations. The undesirable waveforms with a larger IHP are set to zero. Furthermore, the optimum threshold in this approach can be derived from the signal itself using the consecutive mean square error (CMSE). The method is fully data driven, and it requires no prior knowledge of the target signals. This method can be verified with the simulative program by using Matlab. The denoising results are proper. In comparison with other EEMD based methods, it is concluded that the means adopted in this paper is suitable to preprocess the stress wave signals in the wood nondestructive testing.
    Full-text · Article · Nov 2012 · The Scientific World Journal
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