FPGA Implementation of Combination of Error Normalised Sign LMS and Zero Attractor-ENSLMS Algorithms for Denoising ECG Signals

Abstract

The electrical potential of the heart known as Electrocardiogram(ECG) are contaminated by noises like Power Line Interference, Baseline wander and Electrode Motion etc.,. To improve the reliability of the signal, it is necessary to eliminate these noises. Fixed Coefficient filters are very difficult for denoising the ECG signals. The dynamic changes in the ECG signals can be tracked by means of adaptive filters. Different adaptive filters available currently are Least Mean Square (LMS) and Recursive Least Square (RLS) algorithms. The LMS algorithm is considered for its less complexity and efficiency. The computational complexity of the LMS algorithm can be still be improved by using various other algorithms like Sign LMS Algorithm(SLMS), Error Nonlinear Sign LMS algorithm(ENSLMS). When there is a sparse impulse response, the system should dynamically inherit the sparseness level and this is achieved by exploiting the information about the system sparseness. In this paper, a new approach of combining the ENSLMS and ZA-ENLMS is proposed for noise cancellation in ECG. The designed structures are simulated using MATLAB 2014b, Xilinx System Generator and implemented in Virtex 5 FPGA. The performance analysis is done in terms of SNR and computational complexity. The implementation results show that the number of sliced LUT's and the number of slice registers has been reduced to 5.1% and 3.22% respectively compared with the proposed combination.