High resolution Cardiac signal extraction using novel adaptive noise cancelers

Abstract

Adaptive filter is a primary method to filter electrocardiogram (ECG) or Cardiac signal, because it does not need the signal statistical characteristics. In this paper we present various adaptive noise cancelers (ANCs) for cleaning ECG signal based on Least Mean Fourth (LMF) algorithms. LMF algorithm exhibits lower steady state error than the conventional Least Mean Square (LMS) algorithm. This is due to the fact that the excess mean-square error of the LMS algorithm is dependent only on the second order moment of the noise. The second order moment, or variance of the noise evaluates to be the same for all the noise environments. Based upon this other types of mean fourth based algorithms are implemented. These are Normalized LMF (NLMF), Error Normalized LMF (ENLMF) and their block based versions BBNLMF and BBENLMS. Different filter structures are presented to eliminate various artifacts present in the ECG. Finally, we have applied these algorithms on real ECG signals obtained from the MIT-BIH data base. The experiments confirms that the performance of the normalized ANCs are superior to the LMF.