Noise Reduction Technique for ECG Signals Using Adaptive Filters

The ECG finds its importance in the detection of cardiac abnormalities. Noise reduction in ECG signal is an important task of biomedical science. ECG signals are very low frequency signals of about 0.5Hz-100Hz and digital filters are very efficient for noise removal of such low frequency signals. In this Paper an adaptive filter for high resolution ECG Signal is presented which estimate the deterministic component of the ECG Signal and remove the noise. The filter needs two input: the signal (primary input) and an impulse correlated with the deterministic component (reference input). Several signals to noise ratio were considered and the effect of shape variation was also studied. The adaptive filters essentially minimizes the mean-squared error between a primary input, which is the noisy ECG, and a reference input, which is either noise that is correlated in some way with the noise in the primary input or a signal that is correlated only with ECG in the primary input. Different filter structures are presented to eliminate the diverse forms of noise: 60Hz power line interference, baseline wander, muscle noise and the motion artifact. Finally, we have applied these algorithms on real ECG signals obtained from the MIT-BIH data base. Simulation results are also shown. Performance of filters is analyzed based on SNR and MSE.