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

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Abstract

The electrocardiogram (ECG) is widely used for diagnosis of heart diseases. Good quality ECG are utilized by physicians for interpretation and identification of physiological and pathological phenomena. However, in real situations, ECG recordings are often corrupted by artifacts. Two dominant artifacts present in ECG recordings are: (1) high-frequency noise caused by electromyogram induced noise, power line interferences, or mechanical forces acting on the electrodes; (2) baseline wander (BW) that may be due to respiration or the motion of the patients or the instruments. These artifacts severely limit the utility of recorded ECGs and thus need to be removed for better clinical evaluation. Several methods have been developed for ECG enhancement. In this paper, we propose a new ECG enhancement method based on the recently developed empirical mode decomposition (EMD). The proposed EMD-based method is able to remove both high-frequency noise and BW with minimum signal distortion. The method is validated through experiments on the MIT–BIH databases. Both quantitative and qualitative results are given. The simulations show that the proposed EMD-based method provides very good results for denoising and BW removal.

Introduction

The electrocardiogram (ECG) is the recording of the cardiac activity and it is extensively used for diagnosis of heart diseases. It is also an essential tool to allow monitoring patients at home, thereby advancing telemedical applications. Recent contributions in this topic are reported in [1], [2], [3]. Even though these contributions are for different projects, the issue common to each is the use of ECG for remote monitoring and assistance under different telecommunication platforms. The transmission of ECG often introduces noise due to poor channel conditions. Moreover, there are other types of noise inherent in the data collection process. These artifacts are particularly significant during a stress test. The main sources of such artifacts are: (1) the baseline wander (BW) mainly caused by respiration, and (2) high-frequency noise such as the electromyographic (EMG) noise caused by the muscle activity. Moreover, the motion of the patient or the leads affects both types of artifacts. In ECG enhancement, the goal is to separate the valid ECG from the undesired artifacts so as to present a signal that allows easy visual interpretation.

Many approaches have been reported in the literature to address ECG enhancement. Some recent relevant contributions have proposed solutions using a wide range of different techniques, such as perfect reconstruction maximally decimated filter banks [4] and nonlinear filter banks [5], advanced averaging [6], [7], the wavelet transform [8], [9], [10], [11], adaptive filtering [12], singular value decomposition [13], and independent component analysis [14].

In this paper, we propose a new method for ECG enhancement based on the empirical mode decomposition (EMD). The EMD was recently introduced in [15] as a technique for processing nonlinear and nonstationary signals. It also serves as an alternative to methods such as the wavelet analysis, the Wigner–Ville distribution, and the short-time Fourier transform. It is proposed as a preprocessing stage to efficiently compute the instantaneous frequency through the Hilbert transform [16], although it can be applied independently as well.

It is reported in [17] that EMD behaves as a “wavelet-like” dyadic filter bank for fractional Gaussian noise. This conclusion has been applied in a detrending and denoising example in [18]. The work in [19] presents one of the first application of EMD in biomedical engineering, where blood pressure is studied. Regarding ECG signal processing, one of the first EMD-based contributions is [20], which investigates the chaotic nature of ECG. Also related to the cardiac system, the EMD is utilized in the analysis of heart rate variability (HRV) [21], [22]. The EMD is also used for artifact reduction in gastric signals [23]. Finally, in [24], the EMD is utilized to extract the lower esophageal sphincter pressure in the gastroesophageal reflux disease.

As the brief review above demonstrates, the EMD is a good tool for artifact reduction applications. This motivates the proposed use of the EMD for ECG enhancement. In this work, we address both denoising and BW removal based on the EMD.

The contributions of this work lie in two aspects. First, we introduce the use of the EMD in ECG enhancement. Second, noting that both high-frequency noise and BW components are mixed with ECG signal component in the EMD domain, we develop novel methods to remove both types of artifacts.

The performance of the proposed algorithm is demonstrated through various experiments performed over several records from the MIT–BIH arrhythmia database [25]. Quantitative and qualitative experiments are carried out for synthetic and real noise cases. The experimental studies show that the proposed EMD-based method is a good tool for ECG denoising and BW removal, especially for the important real noise cases.

The outline of the paper is as follows. In Section 2 a brief review of the EMD is presented. The algorithms for denoising and baseline removal are explained in 3 ECG denoising using EMD, 4 ECG BW removal using EMD, respectively. Section 5 presents the experimental studies that demonstrate the performances of the proposed method. Finally, conclusions are given in Section 6.

Section snippets

Empirical mode decomposition

The EMD was recently proposed by Huang et al. [15] as a tool to adaptively decompose a signal into a collection of AM–FM components. Traditional data analysis methods, like Fourier and wavelet-based methods, require some predefined basis functions to represent a signal. The EMD relies on a fully data-driven mechanism that does not require any a priori known basis. It is especially well suited for nonlinear and nonstationary signals, such as biomedical signals.

The aim of the EMD is to decompose

ECG denoising using EMD

High-frequency denoising by the EMD is in general carried out by partial signal reconstruction, which is premised on the fact that noise components lie in the first several IMFs. This strategy works well for those signals whose frequency content is clearly distinguished from that of noise and is successfully applied in [18], [24]. The basic idea is to statistically determine the index of the IMFs that contain most of the noise components, beginning from fine to coarse scale. Given the index,

ECG BW removal using EMD

Since BW is a low-frequency phenomenon, it is expected that the major BW components are located in the higher-order IMFs. The residue, which can also be regarded as the last IMF, may not correspond to the BW because the BW may have multiple extrema and zero crossings, which violates the residue definition. Indeed, the BW spreads over the last several IMFs. Simply removing the last several IMFs may introduce significant distortions. Thus, the BW must be separated from the desired components in

Experimental studies

In this section, simulations for several different cases are carried out to evaluate the performance of the proposed EMD-based method. A noisy signal s(t)=x(t)+n(t) is processed to obtain an enhanced reconstructed version x^(t). The corrupted signal s(t) consists of an original clean signal x(t), which is free of noise, and a noise component realization n(t), that can be synthetic or real. Two groups of experiments are presented. The first simulation experiment is performed over synthetic noise

Conclusions

A novel method for ECG enhancement based on the EMD is presented. Both high-frequency noise and BW removal are addressed. Enhancement is achieved through the development of two EMD-based methods to address each type of artifact. The techniques developed are not based on simple partial summation of IMFs, as in previous work. Rather, different IMFs are chosen and processed to successfully achieve the denoising and BW removal. The effectiveness of the EMD in ECG enhancement is shown through

Acknowledgment

M. Blanco-Velasco's work was supported in part by the Fondo de Investigación Sanitaria under project PI052277.

ManuelBlanco-Velasco was born in Saint Maur des Fossés, France, in 1967. He received the engineering degree from the Universidad de Alcalá, Madrid, Spain, in 1990, the MSc in communications engineering from the Universidad Politécnica de Madrid, Spain, in 1999, and the PhD degree from the Universidad de Alcalá in 2004. From 1992 to 2002, he was with the Circuits and Systems Department at the Universidad Politécnica de Madrid as Assistant Professor. In April 2002, he joined the Signal Theory and

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    ManuelBlanco-Velasco was born in Saint Maur des Fossés, France, in 1967. He received the engineering degree from the Universidad de Alcalá, Madrid, Spain, in 1990, the MSc in communications engineering from the Universidad Politécnica de Madrid, Spain, in 1999, and the PhD degree from the Universidad de Alcalá in 2004. From 1992 to 2002, he was with the Circuits and Systems Department at the Universidad Politécnica de Madrid as Assistant Professor. In April 2002, he joined the Signal Theory and Communications Department of the Universidad de Alcalá where he is now working as Associate Professor. His main research interest is biomedical signal processing.

    BinweiWeng received the BE and ME degrees in electrical engineering from Shanghai Jiao Tong University, Shanghai, China, in 1997 and 2000, respectively, and the MS degree in electrical and computer engineering from the University of Iowa, Iowa City, in 2003. He received his Ph.D. degree in Electrical and Computer Engineering from the University of Delaware in 2006. He is currently with Philips Medical Systems in Andover, Massachusetts. His main interests are nonlinear signal processing and biomedical signal processing.

    KennethE.Barner received his B.S.E.E. degree (magna cum laude) from the Lehigh University, Bethlehem, Pennsylvania, in 1987. He received his M.S.E.E. and Ph.D. degrees from University of Delware, Newark, Delaware, in 1989 and 1992, respectively. For his dissertation “Permutation Filters: A Group Theoretic Class of Non-Linear Filters,” Dr. Barner received the Allan P. Colburn Prize in Mathematical Sciences and Engineering for the most outstanding doctoral dissertation in the engineering and mathematical disciplines.

    Dr. Barner was the duPont Teaching Fellow and a Visiting Lecturer at the University of Delaware in 1991 and 1992, respectively. From 1993 to 1997 Dr. Barner was an Assistant Research Professor in the Department of Electrical and Computer Engineering at the University of Delaware and a Research Engineer at the duPont Hospital for Children. He is currently a Professor in the Department of Electrical and Computer Engineering at the University of Delaware. Dr. Barner is the recipient of a 1999 NSF CAREER award. He was the co-chair of the 2001 IEEE-EURASIP Nonlinear Signal and Image Processing (NSIP) Workshop and a Guest Editor for a special issue of the EURASIP Journal of Applied Signal Processing on Nonlinear Signal and Image Processing. Dr. Barner is a member of the Nonlinear Signal and Image Processing Board and is co-editor of the book Nonlinear Signal and Image Processing: Theory, Methods, and Applications, CRC Press, 2004. Dr. Barner was the Technical Program co-Chair for ICASSP 2005 and is currently serving on the IEEE Signal Processing Theory and Methods (SPTM) and IEEE Bio-Imaging and Signal Processing (BISP) technical committees as well as the IEEE Delaware Bay Section Executive Committee. Dr. Barner has served as an Associate Editor of the IEEE Transactions on Signal Processing, the IEEE Transaction on Neural Systems and Rehabilitation Engineering, and the IEEE Signal Processing Magazine. Dr. Barner is currently the Editor-in-Chief of the journal Advances in Human–Computer Interaction, a member of the Editorial Board of the EURASIP Journal of Applied Signal Processing, and is serving as a Guest Editor for that journal on the it Super-Resolution Enhancement of Digital Video and Empirical Mode Decomposition and the Hilbert–Huang Transform special issues. His research interests include signal and image processing, robust signal processing, nonlinear systems, communications, human–computer interaction, haptic and tactile methods, and universal access. Dr. Barner is a Member of Tau Beta Pi, Eta Kappa Nu, and Phi Sigma Kappa.

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