Performance analysis of adaptive filtering algorithms for denoising of ECG signals

Electrocardiogram (ECG) can help to diagnose range of diseases including heart arrhythmias, heart enlargement, heart inflammation (pericarditis or myocarditis) and coronary heart disease. ECG consists of noise which is non stationary that affects the reliability of ECG waveform. In this paper an adaptive filter for denoising ECG signal based on Least Mean Squares (LMS), Normalized Least Mean Square (NLMS), Affine Projection LMS (APA-LMS) and Recursive least Squares algorithm (RLS) is presented with experimental results and the results are found to be encouraging. The performances of these algorithms are compared in terms of various parameters such as SNR, PSNR, MSE and SD. To validate the proposed methods, real time recorded data from the MIT-BIH database is used. RLS algorithm is found to exhibit lower MSE, and higher SNR compared to other algorithms. Therefore the results demonstrate superior performance of adaptive RLS filter for denoising of ECG signal.