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One of the core concerns in the area of Biomedical Signal Processing has been the extraction of pure cardiologic indices from noisy measurements. Frequently, it is found that treatment of the patient suffers due to improper information of Electrocardiogram (ECG) signal since it is highly prone to the disturbances such as noise contamination, artifacts and other signals interference. Therefore, an ECG signal must be denoised so that the misrepresentations can be eliminated from the original signal for the perfect diagnosing of the condition and performance of the heart. In this paper, hybrid techniques including combination of Median filter, Savitzky-Golay filter and Extended Kalman filter along with Discrete Wavelet Transform have been focussed for separation of noise from ECG signal. The hybrid methods for obtaining a clean ECG signal are designed and implemented in MATLAB environment by utilizing MIT-BIH Arrhythmia database. Performance of different algorithms is compared on the basis of signal to noise ratio (SNR) and mean square error (MSE) and it has been noticed that Extended Kalman filter followed by Discrete Wavelet Transform provides better results for both the parameters.