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
Link To Document :
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