ECG signal denoising based on ensemble emd thresholding and higher order statistics

Abstract

In this paper, we propose a new ECG signal enhancement based on Ensemble Empirical Mode Decomposition (EEMD) and Higher Order Statistics (HOS). In our scheme, the EEMD is used to decompose adaptively the noisy ECG signal into Intrinsic Mode Functions (IMFs), and a novel criterion based on kurtosis is proposed to determine the IMFs that contain sufficient information about the QRS complex in ECG signal and which need to be filtered. After that, two EEMD interval thresholding methods have been applied to each selected IMF in order to reduce the noise and to preserve the QRS complex. The final denoised ECG signal is then reconstructed by summing the thresholded IMFs with the retained unprocessed lower frequency IMFs. To assess the usefulness of our approach, we evaluate the performance of the proposed scheme on a set of real ECG signals acquired from MIT-BIH arrhythmia database. The experimental results demonstrate that the proposed method shows better Signal to Noise Ratio (SNR) and lower Mean Square Error (MSE) compared to some of the state-of-the-art denoising methods.