ECG noise filtering using wavelets with soft-thresholding methods

We analyze algorithms for filtering white gaussian noise and 50 Hz power line noise from ECG signals. We used several wavelets to study their effect and efficiency in the filtering process. To deal with these different kinds of noises we used two distinct soft-thresholding techniques: the Donoho's statistical threshold estimator and a method developed by us. This last method exploits one of the wavelet processing main features: time-frequency relation. The de-noising methods led to good filtering results: noise reduction with only minor change of the ECG waveforms as confirmed by the high correlation values between the processed signal and the original one. These good results are due to a wavelet advantage over classical filtering-time-frequency relation-enabling the possibility of filtering noise in the same frequency band of the ECG signal with minimal interference.