Author : P Yadav, KP Gowd, P.S. Singhel, A Khare and SK Paranjpe

 
In recent years, adaptive filtering has become one of the effective and popular approaches for the processing and analysis of the signals with noise especially of the biomedical signals. Adaptive filters permit to detect time varying potentials and to track the dynamic variations of the signals. Besides, they modify their behavior according to the input signal. Therefore, they can detect shape variations in the ensemble and thus they can obtain a better signal estimation. The aim of this paper is to study, analyze various adaptive filter algorithms and apply Mat lab to investigate their performance behaviors with two step sizes of 0.02 and 0.04. Further to remove motion artifacts from Electrocardiogram signal as an application of this concepts. At the end of this paper, a performance study has been done between these algorithms based on various step sizes. It has been found that there will be always tradeoff between step sizes and Mean square error. The Electrocardiogram signals used in this paper are from the MIT-BIH database. Elimination of noises from Electrocardiogram signal example is a classical problem.

Keywords: Adaptive filter, Least Mean Square (LMS), Normalized LMS (NLMS), Block LMS (BLMS), Sign LMS (SLMS), Signed Regressor LMS (SRLMS), Motion artifact.

Article published in International Journal of Current  Engineering  and Technology, Vol.3,No.3(Aug- 2013)