Full Text:   <3853>

CLC number: TN911.72; R318.04

On-line Access: 2011-05-09

Received: 2010-08-27

Revision Accepted: 2010-11-11

Crosschecked: 2011-03-31

Cited: 5

Clicked: 3831

Citations:  Bibtex RefMan EndNote GB/T7714

-   Go to

Article info.
1. Reference List
Open peer comments

Journal of Zhejiang University SCIENCE C 2011 Vol.12 No.5 P.397-403

http://doi.org/10.1631/jzus.C1010311


Removal of baseline wander from ECG signal based on a statistical weighted moving average filter


Author(s):  Xiao Hu, Zhong Xiao, Ni Zhang

Affiliation(s):  School of Mechanical and Electric Engineering, Guangzhou University, Guangzhou 510006, China, Guangdong General Hospital, Guangzhou 510080, China

Corresponding email(s):   huxiao@gzhu.edu.cn

Key Words:  ECG signal, Baseline wander, Morphological feature, Moving average filter, Wavelet package translation


Xiao Hu, Zhong Xiao, Ni Zhang. Removal of baseline wander from ECG signal based on a statistical weighted moving average filter[J]. Journal of Zhejiang University Science C, 2011, 12(5): 397-403.

@article{title="Removal of baseline wander from ECG signal based on a statistical weighted moving average filter",
author="Xiao Hu, Zhong Xiao, Ni Zhang",
journal="Journal of Zhejiang University Science C",
volume="12",
number="5",
pages="397-403",
year="2011",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.C1010311"
}

%0 Journal Article
%T Removal of baseline wander from ECG signal based on a statistical weighted moving average filter
%A Xiao Hu
%A Zhong Xiao
%A Ni Zhang
%J Journal of Zhejiang University SCIENCE C
%V 12
%N 5
%P 397-403
%@ 1869-1951
%D 2011
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1010311

TY - JOUR
T1 - Removal of baseline wander from ECG signal based on a statistical weighted moving average filter
A1 - Xiao Hu
A1 - Zhong Xiao
A1 - Ni Zhang
J0 - Journal of Zhejiang University Science C
VL - 12
IS - 5
SP - 397
EP - 403
%@ 1869-1951
Y1 - 2011
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C1010311


Abstract: 
baseline wander is a common noise in electrocardiogram (ECG) results. To effectively correct the baseline and to preserve more underlying components of an ECG signal, we propose a simple and novel filtering method based on a statistical weighted moving average filter. Supposed a and b are the minimum and maximum of all sample values within a moving window, respectively. First, the whole region [a, b] is divided into M equal sub-regions without overlap. Second, three sub-regions with the largest sample distribution probabilities are chosen (except M<3) and incorporated into one region, denoted as [a0, b0] for simplicity. Third, for every sample point in the moving window, its weight is set to 1 if its value falls in [a0, b0]; otherwise, its weight is 0. Last, all sample points with weight 1 are averaged to estimate the baseline. The algorithm was tested by simulated ECG signal and real ECG signal from www.physionet.org. The results showed that the proposed filter could more effectively extract baseline wander from ECG signal and affect the morphological feature of ECG signal considerably less than both the traditional moving average filter and wavelet package translation did.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Boucheham, B., Ferdi, Y., Batouche, M.C., 2005. Piecewise linear correction of ECG baseline wander: a curve simplification approach. Comput. Methods Programs Biomed., 78(1):1-10.

[2]Burattini, L., Zareba, W., Burattini, R., 2006. The effect of baseline wandering in automatic T-wave alternans detection from holter recordings. Comput. Cardiol., 33:257-260.

[3]Chen, H.Y., Huang, M., Jiang, Y.X., Hai, J., 2006. Detection of ST segment in electrocardiogram by wavelet transform. Electr. Mach. Control, 10(5):531-533 (in Chinese).

[4]Clifford, G.D., Azuaje, F., McSharry, P.E., 2006. Advanced Methods and Tools for ECG Data Analysis. Artech House, Norwood, MA, p.135-191.

[5]Coifman, R.R., Wickerhauser, M.V., 1992. Enthropy-based algorithms for best basis selection. IEEE Trans. Inform. Theory, 38(2):713-718.

[6]Hu, X., Gao, Y., Liu, W.X., 2009. Pattern recognition of surface electromyography signal based on wavelet coefficient entropy. Health, 1(2):121-126.

[7]Hu, X., Xiao, Z., Liu, C.H., 2010. Reduction Arithmetic for Power Line Interference from ECG Based on Estimating Sinusoidal Parameters. 3rd Int. Conf. on Biomedical Engineering and Informatics, p.2089-2092.

[8]Jeong, G.Y., Yu, K.H., Yoon, M.J., Inooka, E., 2010. ST shape classification in ECG by constructing reference ST set. Med. Eng. Phys., 32(9):1025-1031.

[9]Leski, J.M., Henzel, N., 2005. ECG baseline wander and powerline interference reduction using nonlinear filter bank. Signal Process., 85(4):781-793.

[10]Momot, A., 2009. Methods of weighted averaging of ECG signals using Bayesian inference and criterion function minimization. Biomed. Signal Process. Control, 4(2):162-169.

[11]NIBIB and NIGMS, 1999. PhysioNet Resource. National Institutes of Health. Available from http://www.physionet.org/

[12]Shi, L., Yang, C.Y., Fei, M.R., 2008. Electrocardiogram R-wave and ST segment extraction based on wavelet transform. Chin. J. Sci. Instrum., 29(4):804-809 (in Chinese).

[13]Sörnmo, L., 1993. Time-varying digital filtering of ECG baseline wander. Med. Biol. Eng. Comput., 31(5):503-508.

[14]Xu, L.S., Zhang, D., Wang, K.Q., Li, N.M., Wang, X.Y., 2007. Baseline wander correction in pulse waveforms using wavelet-based cascaded adaptive filter. Comput. Biol. Med., 37(5):716-731.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - Journal of Zhejiang University-SCIENCE