Accurate analysis of ECG signals becomes difficult when a lot of noise such as AC (Power line) Interference, Electromyogram (EMG), Baseline wandering, channel noise, electrode motion, motion artifact, Gaussian noise & high frequency noise based on the frequency variation are present in the ECG signal. Thus, for better analysis and characterization of ECG, noise removal becomes an essential part. Denoising of ECG signals plays a very important role in diagnosis and detection of various cardiovascular diseases. The various methods available for denoising of ECG signals include linear filtering, Empirical Mode Decomposition (EMD), Independent and Principal Component Analysis, Neural networks, adaptive filtering etc. In recent studies by several researchers compared to the above mentioned denoising methods Discrete Wavelet Transform (DWT) and Ensemble Empirical Mode reducing noise from ECG signal. This paper presents the performance analysis on ECG denoising algorithms in EEMD and wavelet domains by evaluating Signal to Noise Ratio (SNR) and Root Mean Square Error (RMSE) in order to compare the effectiveness of these two methods in reducing the noise. 

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