DocumentCode
2627741
Title
ECG Signal Preprocessing Based on Change Step Iteration of the LMS Adaptive Filtering Algorithm
Author
Ya, Tu ; Runjing, Zhou ; Fei, Zhang
Author_Institution
Dept. of Autom., Inner Mongolia Univ., Hohhot, China
Volume
6
fYear
2009
fDate
March 31 2009-April 2 2009
Firstpage
155
Lastpage
159
Abstract
For there are power line interference and baseline drift to ECG signal, the signal to noise ratio of ECG is reduced enormously. Based on self-adaptive noise cancellation system, a step iteration changeable least mean square (LMS) algorithm is brought forward. The step-change factor is introduced in the process of the algorithm. The concrete changing process of the noise variance and the signal error are both considered, and the step is adjusted with the increase in the number of iterative to match with the convergence of adaptive filter. The effect of the algorithm in this paper is compared with that of the other LMS algorithm at the same time. The results show that the step iteration changeable algorithm can be implemented easily and its computational complexity is smaller. In addition, the error convergence rate of the algorithm is fast, and the filtering effect is good.
Keywords
adaptive filters; computational complexity; electrocardiography; iterative methods; least mean squares methods; medical signal processing; signal denoising; ECG signal preprocessing; LMS adaptive filtering algorithm; baseline drift; change step iteration; computational complexity; error convergence rate; least mean square algorithm; noise variance; power line interference; self-adaptive noise cancellation system; signal-to-noise-ratio; Adaptive filters; Concrete; Convergence; Electrocardiography; Filtering algorithms; Interference; Iterative algorithms; Least squares approximation; Noise cancellation; Signal to noise ratio;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Science and Information Engineering, 2009 WRI World Congress on
Conference_Location
Los Angeles, CA
Print_ISBN
978-0-7695-3507-4
Type
conf
DOI
10.1109/CSIE.2009.729
Filename
5170680
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