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Efficient Cardiac Signal Enhancement Techniques Based on Variable Step Size and Data Normalized Hybrid Signed Adaptive Algorithms

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In remote health care monitoring, the extraction of high resolution cardiac signals is an important task. For this the cardiac signal (CS) needed to be enhanced. Among various filtering techniques, the adaptive noise cancellation (ANC) is a promising methodology. In adaptive filtering least mean square (LMS) algorithm is the fundamental enhancement algorithm. However, it suffers with slow convergence and weight drift problem in non-stationery environment. In order to improve the performance of ANC this paper proposes to implement an ANC methodology based on variable step size on the normalization of fundamental LMS algorithm for CS enhancement. Based on such strategy this research implements ANC using hybrid algorithm called variable normalized LMS (VNLMS) algorithm. Further, to improve the convergence characteristics, to filter the ability and to minimize computational complexity some versions of VNLMS algorithms are implemented too. Finally, the proposed ANCs are tested using real CS obtained from MIT-BIH data base. The performance evaluation is carried based on signal to noise ratio improvement (SNRI) and excess mean square error (EMSE).
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Arrhythmia; Artifacts; Convergence; Computational Complexity; Least Mean Square

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