A study of recursive least squares (RLS) adaptive filter algorithm in noise removal from ECG signals

Electrocardiogram (ECG) is a diagnostic procedure that measures and records the electrical activity of heart in detail. By reviewing an ECG report, one's condition of heart can be evaluated. But ECG signals are often affected and altered by the presence of various noises that degrade the accuracy of an ECG signal and thus misrepresents the recorded data. To filter out these noises conventional digital filters have been used for decades. Yet noise cancellation with finite and determined coefficients has often been unsuccessful due to the non-stationary nature of ECG signal. Adaptive filters adapt their filter coefficients with the continuous change of signal using adaptive algorithms, providing the optimum noise removal features for non-stationary signals like ECG. In this study, the adaptive filter algorithm, RLS has been used in cancellation of various noises in ECG signals. We have also performed noise removal using LMS adaptive filter algorithm to compare the performance of RLS algorithm. We have used MATLAB® to simulate different noise signals and process the noises. The ECG signals used here have been taken from the PhysioNet ECG-ID database. The simulation results depict that RLS algorithm renders a much better performance in removing noises from the ECG signals than LMS algorithm.