Low Complex Adaptive Fast Converged Kalman Filter for Cardiac ECG Artifacts Elimination

Abstract

The aim of this paper is to reduce the main problems in biomedical data processing like electrocardiography is the separation of the wanted signal from noises caused by power line interference, external electromagnetic fields, random body movements and respiration. Different types of digital filters are used to remove signal components from unwanted frequency ranges. It is difficult to apply filters with fixed coefficients to reduce Biomedical Signal noises, because human behavior is not exact known depending on the time. Adaptive filter technique is required to overcome this problem. In this paper different types of adaptive filters are considered to reduce the ECG signal noises like Base Line Interference, EM noise and muscle artifact, results of simulations in MATLAB are presented. While the LMS algorithm and its normalized version (NLMS), have been thoroughly used and studied. Connections between the Kalman filter and the RLS algorithm have been established however, the connection between the Kalman filter and the LMS algorithm has not received much attention. By linking these two algorithms, a new normalized Kalman based LMS (KLMS) algorithm can be derived that has some advantages to the classical one. Their stability is guaranteed since they are a special case of the Kalman filter. More, they suggests a new way to control the step size, that results in good convergence properties for a large range of input signal powers, that occur in many applications. In this paper, different algorithms based on the correlation form, information form (IKLMS) and simplified versions (SIKLMS) of these are presented. The simplified form maintains the good convergence properties of the KLMS with slightly lower computational complexity.