International Journal of Electrical and Computer Engineering (IJECE) Vol. 9, No. 1, February 2019, pp. xx~xx ISSN: 2088-8708, DOI: 10.11591/ijece.v9i1.pp.xx-xx  101 Journal homepage: http://iaescore.com/journals/index.php/IJECE ECG Signal Denoising Using a novel Approach of Adaptive Filters for Real-Time Processing Amean Al-Safi 1 1 Department of Electrical and Electronics Engineering, University of Thi-Qar, Iraq Article Info ABSTRACT Article history: Received Aug 16, 2019 Revised Feb 2, 2020 Accepted Feb 28, 2020 Electro Cardio Gram (ECG) is considered as the main signal that can be used to diagnose different kinds of diseases related to human heart. During the recording process, it is usually contaminated with different kinds of noise which includes Power-Line Interference, Baseline Wandering and Muscle Contraction. In order to clean the ECG signal, several noise removal techniques have been used such as adaptive filters, empirical mode decomposition, Hilbert-Huang transform, Wavelet-Based algorithm, Discrete Wavelet Transforms, Modulus Maxima of Wavelet Transform, Patch Based Method, and many more. Unfortunately, all the presented methods cannot be used for online processing since it takes long time to clean the ECG signal. The current research presents a unique method for ECG denoising using a novel approach of adaptive filters. The suggested method was tested by using a simulated signal using MATLAB software under different scenarios. Instead of using a reference signal for ECG signal denoising, the presented model uses a unite delay and the primary ECG signal itself. LMS (Least Mean Square), NLMS (Normalized Least Mean Square), and Leaky LMS were used as adaptation algorithms in this paper. Keyword: Adaptive filters ECG ECG signal denoising LMS NLMS Leaky LMS Copyright © 2019 Institute of Advanced Engineering and Science. All rights reserved. Corresponding Author: Amean Al-Safi, Department of Electrical and Electronics Engineering, University of Thi-Qar, Thi-Qar, Iraq Email: ameansharea.ghazi@wmich.edu 1. INTRODUCTION Electrocardiograms (ECG) signals contain different kinds of information that can be used to diagnose various heart related diseases. They reflect the electrical activity of the human heart. ECG signals are usually contaminated by various types of noise and artifacts. Power line interference (PLI), baseline wander, drift in electrodes connections (electrode misconduct noise and electrode displacement artifacts), and muscle artifacts are the most effective ones. They make the diagnosing process and obtaining the required signal information a hard task to reach [1]-[2]. Performing any kinds of physical analysis to ECG signals should be proceeded by signal denoising process since this kind of analysis might result in wrong diagnosis of cardiac arrhythmias [3]– [8]. In order to remove the contamination noise (denoising) from the recorded ECG signal, several methods have been presented. ECG signal denoising techniques have been designed based on median filters, adaptive filters, Wiener filters, switching Kalman filters, polynomial filters, frequency-selective filters, SVD (singular value decomposition), DWT (discrete wavelet transform), DCT (discrete cosine transform), EMD (empirical mode decomposition), NBF (nonlinear Bayesian filter), MM (mathematical morphological) operators, PCA(principal component analysis), ICA(independent component analysis), NLM (nonlocal mean) technique, VMD(variational mode decomposition), and EMD based technique for single and combined noise sources removal which might be considered as the most recent technique for ECG signal denoising [9]-[10].