PLI Removal from ECG Signal using Adaptive Algorithms

Abstract

Electrocardiogram (ECG) is the graphical illustration of heart activity to diagnose various cardiovascular diseases. Presence of Power Line Interference (PLI) in ECG makes it difficult for the examiner to identify proper working of heart. To remove such interference different types of adaptive noise cancellers have been implemented. All the adaptive algorithms previously implemented for such purpose have either better convergence, mean square error (MSE) or better complexity. So a new algorithm named SSLMS is implemented to have a compromise between the previously mentioned parameters. Using SSLMS, first impulsive component of PLI has been removed and comparison of it has been made with NLMS, RLS and SSRLS algorithms. In later work, PLI having known frequency is estimated using sinusoidal model of SSLMS algorithm and comparisons are made with SSRLS algorithm. Later PLI with unknown frequency is being tracked by first converging to its true frequency and then estimating it based to the new value of frequency. In the end PLI with unidirectional and bidirectional frequency is being estimated and removed from ECG signal. Moreover, every simulation using SSLMS has also been compared with those of SSRLS algorithm. As SSRLS has better convergence and MSE but exceptionally high computational complexity than that of SSLMS algorithm, so a new hybrid algorithm is proposed that combines the best features of both SSRLS and SSLMS algorithms. This algorithm has faster convergence than that of SSLMS algorithm and lower computational complexity than SSRLS algorithm. Moreover, its MSE is lower than those of both SSRLS and SSLMS algorithm. Simulation results prove the enhanced performance of the proposed hybrid.