Denoising of ECG signals using Empirical Mode Decomposition based technique

The Electrocardiogram (ECG) shows the electrical activity of the heart and is used by physicians to inspect the heart's condition. Analysis of ECG becomes difficult if noise is embedded with signal during acquisition. In this paper, a denoising technique for ECG signals based on Empirical Mode Decomposition (EMD) is proposed. The noisy ECG signal is initially decomposed into a set of Intrinsic Mode Functions (IMFs) using EMD method. In the proposed technique, the IMFs which are dominated by noise are automatically determined using Spectral Flatness (SF) measure and then filtered using butterworth filters to remove noise. This method is evaluated on ECG signals available in MIT-BIH Arrhythmia database. The experiment results show that the proposed technique performs with better Signal to Noise Ratio (SNR) and lower Root Mean Square Error (RMSE) than the commonly used Wavelet Transform based denoising technique.