ECG(Electrocardiogram) signals get corrupted due to different noises and artifacts such as power line interference, baseline wander, motion artifacts, contact noise that hide lot of required information. This information is required for detecting various cardiac diseases. Hence before processing ECG signal denoising plays important role to obtain significant features of ECG signal. For denoising, Independent Component Analysis (ICA) is applied on contaminated ECG signal. ICA is a blind source separation technique used to find the independent source signal from non-gaussian noisy signal. The ICA technique deals with maximization of non-gaussian source signal using higher order parameters such as kurtosis and Negentropy. The process of maximization is based on the central limit theorem. ICA can be used for ECG denoising only because ECG signal has super Gaussian shape. This technique is capable to remove noise and artifacts even though they have same frequency as original signal. The FastICA with different granularity functions like tanh, power 3 isolates noise and original signal using kurtosis values of separated signal. Analysis of FastICA shows tanh granularity has faster convergence as compared to power 3 granularity and SNR ratio is of 7db. © 2016 IEEE.