MATLAB Simulation Comparison for Different Adaptive Noise Cancelation Algorithms Mostafa Guda, Safa Gasser and Mohamed S. El Mahallawy Department of Electronics & Communications Engineering Arab Academy for Science, Technology and Maritime Transport, Egypt mostafa.guda@hotmail.com, safagasser@aast.edu, mahallawy@aast.edu ABSTRACT Electrocardiographic (ECG) signal can be contaminated by diverse forms of noise: baseline wander, 60 Hz power line interference, muscle noise, and motion artifact. 60Hz power line interference can be cancelled using two different approaches; an adaptive filter or notch filters. The adaptive filter 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 we present a MATLAB simulation comparison between different adaptive filter algorithms; Least Mean Square (LMS), Normalised LMS (NLMS), Variable Step size LMS (VSLMS), Recursive Least Square (RLS) and Blind LMS. The comparison is carried out in terms of both, MSE and the algorithm convergence rate. KEYWORDS ECG;ANC;LMS;NLMS;VSLMS;RLS;MSE;Blind LMS 1 Introduction The Electrocardiographic (ECG) signal is the physical interpretation of the electrical behavior created by the heart muscles. This electrical activity of the heart is captured over time by an external electrode attached to the skin and recorded by a device external to the body. The ECG signal frequency ranges from 0.5 to 100 Hz [1]. The ECG signal is normally corrupted with two major types of noise generated by biological and environmental resources. The first type includes muscle contraction, electromyographic (EMG) interference, baseline drift, ECG amplitude modulation due to respiration, and motion artefacts caused by changes in the electrode-skin impedance with electrode motion. The second type includes power line interference, electrode contact noise, instrumentation noise generated by electronic devices used in signal processing, electrosurgical noise, and radio-frequency [2],[3]. One common type of noise that occurs during ECG collection is power line interference of 60Hz. ECG noise cancellation has long puzzled the research community. There has been a tremendous amount of research on how to cancel noise in an ECG signal. In [4] the authors used an Adaptive Noise Canceller (ANC) using the Least Mean Square (LMS) algorithm. The LMS algorithm was devised by Widrow and Hoff in 1959. The advantage of using the algorithm in [4] is its low computational complexity, while it lacks the high convergence speed due to its fixed step size. The authors in [5], [6] introduced another ANC based on the Normalized LMS (NLMS) algorithm. This algorithm has a higher convergence rate than the algorithm used in [4]. The NLMS algorithm is also based on a fixed step size as the LMS. In [7] the Variable Step size LMS was simulated. This algorithm has better estimation ability but it also has high computational complexity compared to the algorithms used in [4], [5] and [6]. ISBN:978-0-9891305-6-1 ©2014 SDIWC 68