Denoising ECG signal using combination of ENSLMS and ZA-LMS algorithms

Electrocardiogram (ECG) signals are affected by various types of noises that are differed based on frequency content. In order to improve accuracy and reliability, it is essential to remove such a disturbance. The denoising of ECG signals is challenging as it is difficult to apply filters with fixed coefficients. Adaptive filtering techniques can be used, in which the filter coefficients can be modified to record the dynamic changes of the signal. The system changes with a sparsity level such as non-sparse, semisparse and sparse. A new approach combination of Least Mean Square (LMS) and Zero Attractor LMS (ZA-LMS) filter is proposed to be suitable for sparse and also for non-sparse environments. It also classifies the system which adjusts to the sparseness level of the system. But later LMS filter was modified with Error Nonlinear Sign LMS (ENSLMS) filter with help of this, SNR gets improved. The performance of these algorithms is simulated using Xilinx system generator and the obtained SNR´s are compared.