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The Scientific World Journal
Volume 2013 (2013), Article ID 896056, 10 pages
http://dx.doi.org/10.1155/2013/896056
Research Article

A Hierarchical Method for Removal of Baseline Drift from Biomedical Signals: Application in ECG Analysis

1Department of Computer Science, School of Engineering, Virginia Commonwealth University, 401 West Main Street, Richmond, VA 23284, USA
2Department of Electrical and Computer Engineering, School of Engineering, Virginia Commonwealth University, 401 West Main Street, Richmond, VA 23284, USA
3Department of Biomedical Engineering, School of Engineering, Virginia Commonwealth University, 401 West Main Street, Richmond, VA 23284, USA
4Department of Emergency Medicine and Michigan Critical Injury and Illness Research Center, University of Michigan, Ann Arbor, MI 48109, USA

Received 12 February 2013; Accepted 9 April 2013

Academic Editors: G. Koch, J. Ma, and V. Positano

Copyright © 2013 Yurong Luo et al. 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.

Abstract

Noise can compromise the extraction of some fundamental and important features from biomedical signals and hence prohibit accurate analysis of these signals. Baseline wander in electrocardiogram (ECG) signals is one such example, which can be caused by factors such as respiration, variations in electrode impedance, and excessive body movements. Unless baseline wander is effectively removed, the accuracy of any feature extracted from the ECG, such as timing and duration of the ST-segment, is compromised. This paper approaches this filtering task from a novel standpoint by assuming that the ECG baseline wander comes from an independent and unknown source. The technique utilizes a hierarchical method including a blind source separation (BSS) step, in particular independent component analysis, to eliminate the effect of the baseline wander. We examine the specifics of the components causing the baseline wander and the factors that affect the separation process. Experimental results reveal the superiority of the proposed algorithm in removing the baseline wander.