Abstract:The electrocardiogram is a technique of recording bioelectric currents generated by the heart which is useful for diagnosing many cardiac diseases. The feature extraction and denoising of ECG are highly useful in cardiology. ECG is a non-stationary signal and it is used for the primary diagnosis of cardiac abnormalities like arrhythmia, myocardial infarction and conduction defects. But the ECG signal often contaminated by different noises. The ECG signal must be denoised to remove all the noises such as Additive White Gaussian noises. This paper deals with the analysis of ECG signal denoising using Wavelet Transform . Different ECG signals from MIT/BIH arrhythmia database are used with added AWG noise. Soft thresholding technique is employed in the signal and the result were evaluated using matlab. The Biorthogonal wavelet transform is applied on the different signal and the performance is evaluated in terms of PRD(percent root difference), PRD improvement (PRD i), SNR(signal to noise ratio),SNR improvement (SNRi)and compression ratio.