Abstract: Electrocardiogram (ECG) is an important biomedical signal for analysing electrical activity of the heart during its contraction and expansion. Analysis of ECG becomes difficult if noise is augmented with the signal during acquisition. During recent years, several denoising techniques were analysed within the field of signal processing. In this paper, non-local means (NLM) filtering technique is explored for denoising the ECG signal and results are developed using Matlab coding. Non local means (NLM) uses concept of self-similarity. Due to the nature of the algorithm, the most favourable case for the NLM is the periodic case, like signals, a straight edge, curved edge, texture images and a complete line of pixels with a similar configuration. The noisy ECG signals are synthesized by adding pulse signals and are then denoised at different levels by optimizing various NLM parameters. The experimental results showed that the proposed technique successfully denoised the noisy ECG signals by selecting appropriate input NLM parameters. Finally, the power signal to noise ratio (PSNR) and mean square error (MSE) were also evaluated.

Keywords: Non local means, ECG, denoising, filter, self-similarity, biomedical signals, peak signal to noise ratio, mean square error.