Review of ECG signal de-noising techniques

The aim of this study is to compare different algorithms destined to de-noise an ECG signal, starting with a normal FIR filter to eliminate the noise. Three other filtering methods are used: a Kalman filter and an LMS filter alongside with the undecimated wavelet transform. These three are considered as adaptive filters that essentially minimize the mean-squared error between the noisy ECG and a reference input, which is either noise that is correlated with the noise in the primary input or a signal that is correlated only with ECG in the primary input. To have a better view of the results, two random ECG signals from the Physiobank database are used.