An efficient ECG denoising methodology using empirical mode decomposition and adaptive switching mean filter

Electrocardiogram (ECG) is a widely employed tool for the analysis of cardiac disorders. A clean ECG is often desired for proper treatment of cardiac ailments. However, in the real scenario, ECG signals are corrupted with various noises during acquisition and transmission. In this article, an efficient ECG denoising methodology using combined empirical mode decomposition (EMD) and adaptive switching mean filter (ASMF) is proposed. The advantages of both EMD and ASMF techniques are exploited to reduce the noises in the ECG signals with minimum distortion. Unlike conventional EMD based techniques, which reject the initial intrinsic mode functions (IMFs) or utilize a window based approach for reducing high-frequency noises, here, a wavelet based soft thresholding scheme is adopted for reduction of high-frequency noises and preservation of QRS complexes. Subsequently, an ASMF operation is performed to enhance the signal quality further. The ECG signals of standard MIT-BIH database are used for the simulation study. Three types of noises in particular white Gaussian noise, Electromyogram (EMG) and power line interference contaminate the test ECG signals. Three standard performance metrics namely output SNR improvement, mean square error, and percentage root mean square difference measure the efficacy of the proposed technique at various signal to noise ratio (SNR). The proposed denoising methodology is compared with other existing ECG denoising approaches. A detail qualitative and quantitative study and analysis indicate that the proposed technique can be used as an effective tool for denoising of ECG signals and hence can serve for better diagnostic in computer-based automated medical system.