Noise Cancellation in ECG Signals using Simplified Adaptive Filtering Techniques.

Heart related problems are increasing day by day and ECG signal are very important in diagnosis of heart related problems. There are various artifact which get added in these signals and changes the original signal, therefore there is a need of removal of these artifact from the original. Several signed LMS based adaptive filters, which are computationally superior having multiplier free weight update loops are used for noise cancellation in the ECG signal. 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. In this paper the performance of the signed regressor LMS algorithm is superior than conventional LMS algorithm, the performance of signed LMS and sign-sign LMS based realizations are comparable to that of the LMS based filtering techniques in terms of computational complexity.