Multiadaptive Bionic Wavelet Transform: Application to ECG Denoising and Baseline Wandering Reduction
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Research Article Multiadaptive Bionic Wavelet Transform: Application to ECG Denoising and Baseline Wandering Reduction Omid Sayadi and Mohammad B. Shamsollahi Biomedical Signal and Image Processing Laboratory (BiSIPL), School of Electrical Engineering, Sharif University of Technology, P.O. Box 11365-9363, Tehran, Iran Received 7 May 2006; Revised 22 October 2006; Accepted 11 January 2007 Recommended by Maurice Cohen We present a new modified wavelet transform, called the multiadaptive bionic wavelet transform (MABWT), that can be applied to ECG signals in order to remove noise from them under a wide range of variations for noise. By using the definition of bionic wavelet transform and adaptively determining both the center frequency of each scale together with the T-function, the problem of desired signal decomposition is solved. Applying a new proposed thresholding rule works successfully in denoising the ECG. Moreover by using the multiadaptation scheme, lowpass noisy interference effects on the baseline of ECG will be removed as a direct task. The method was extensively clinically tested with real and simulated ECG signals which showed high performance of noise reduction, comparable to those of wavelet transform (WT). Quantitative evaluation of the proposed algorithm shows that the average SNR improvement of MABWT is 1.82 dB more than the WT-based results, for the best case. Also the procedure has largely proved advantageous over wavelet-based methods for baseline wandering cancellation, including both DC components and baseline drifts. Copyright © 2007 O. Sayadi and M. B. Shamsollahi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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INTRODUCTION
The heart is a hollow muscular organ which through a coordinated muscle contraction generates the force to circulate blood throughout the body. Each beat of our heart is triggered by an electrical impulse from special sinus node cells in the atrium. The electrical impulse travels to other parts of the heart and causes the heart to contract. An electrocardiogram (ECG) records these electrical signals. A normal ECG describes the electrical activity in the heart, and can be decomposed in characteristic components, named the P, Q, R, S, and T waves. Each of these components has its own typical form and behavior and each heart beat traces the familiar morphology labeled by these peaks and troughs as shown in Figure 1. When an electrocardiogram is recorded, it would be contaminated with many kinds of noise [1], such as the following. (i) Baseline wandering, which can be modeled by low pass noise. (ii) 50 or 60 Hz power-line interference.
(iii) Electromyogram (EMG), which is an electric signal caused by the muscle motion during effort test. (iv) Motion artifact, which comes from the variation of electrode-skin contact impedance produced by electrode movement during effort test. Since ECG is mostly contaminated
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