ECG signal denoising based on Empirical Mode Decomposition and moving average filter

Electrocardiogram (ECG) signal shows the electrical activity of the heart and provides useful information that helps in analyzing the patient's heart condition. But different noises get contaminated with ECG signal during its acquisition and transmission, which can cause a great deal of hindrance to manual and automatic analysis of ECG signals and they may be interpreted as the abnormal heart conditions. Hence for the proper diagnosis of the heart the ECG signals must be free of noises. In this work denoising of the ECG signal is the major objective and technique used for this purpose is based on the Empirical Mode Decomposition (EMD) followed by moving average filter. The proposed method is an enhancement towards the existing EMD based denoising algorithms. EMD is an adaptive and data driven technique, thus suitable for any nonstationary signal. For denoising, the ECG signal is initially decomposed into a set of Intrinsic Mode Functions (IMFs), then high frequency noises are eliminated using lower order IMFs followed by the reconstruction of the ECG signal and it is found to be free of noises with a high degree of Signal to Error Ratio (SER). In this work white Gaussian noise is considered and results obtained by simulations show both qualitatively as well as quantitatively that the approach used here is really a very effective and promising one for denoising the ECG signals without losing its actual characteristics.