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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

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


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.

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author="Xiao Hu, Zhong Xiao, Ni Zhang",
journal="Journal of Zhejiang University Science C",
publisher="Zhejiang University Press & Springer",

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%T Removal of baseline wander from ECG signal based on a statistical weighted moving average filter
%A Xiao Hu
%A Zhong Xiao
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%DOI 10.1631/jzus.C1010311

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
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DOI - 10.1631/jzus.C1010311

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


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