Denoising of Electrocardiogram (ECG) signal by using empirical mode decomposition (EMD) with non-local mean (NLM) technique

In this paper, the investigation on effectiveness of the empirical mode decomposition (EMD) with non-local mean (NLM) technique by using the value of differential standard deviation for denoising of ECG signal is performed. Differential standard deviation is calculated for collecting information related to the input noise so that appropriate formation in EMD and NLM framework can be performed. EMD framework in the proposed methodology is used for reduction of the noise from the ECG signal. The output of the EMD passes through NLM framework for preservation of the edges and cancel the noise present in the ECG signal after the EMD process. The performance of the proposed methodology has been validated by using added white and color Gaussian noise to the clean ECG signal from MIT-BIH arrhythmia database at different signal to noise ratio (SNR). The proposed denoising technique shows lesser mean of percent root mean square difference (PRD), mean square error (MSE), and better mean SNR improvement compared to other well-known methods at different input SNR. The proposed methodology also shows lesser standard deviation PRD, MSE, and SNR improvement compared to other well-known methods at different input SNR. (C) 2018 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.