Adaptive Filter Optimized by PSO Algorithm for Denoising of ECG Signals

ECG signals noise removal is an important field in biomedical science. In numerous applications of signal processing, communications and biomedical we are faced with the necessity to remove noise and distortion from the signals. Adaptive filtering is one of the most important areas in digital signal processing to remove background noise and distortion from ECG signals. In last few years various adaptive algorithms are developed for noise cancellation. In this paper we have presented an implementation of LMS (Least Mean Square), NLMS (Normalized Least Mean Square) and proposed PSO-LMS (Particle Swarm Optimization-Least Mean Square) algorithms on MATLAB latform with the intention to compare their performance in ECG noise cancellation application. We simulate the adaptive filter in MATLAB with a noisy ECG signal and analyze the performance of algorithms in terms of MSE (Mean Squared Error), SNR Improvement, computational complexity and correlation between result and template ECG. The obtained results show that, the proposed PSO-LMS algorithm eliminates more noise from noisy ECG signal and has the best performance but at the cost of large computational complexity and higher memory requirements.

Keywords:

LMS , Adaptive Filter , ECG Signals , PSO NLMS