A Hierarchical Method for Removal of Baseline Drift from Biomedical Signals: Application in ECG Analysis. (20th May 2013)

• Record Type: Journal Article
• Authors: Luo, Yurong; Hargraves, Rosalyn H.; Belle, Ashwin; Bai, Ou; Qi, Xuguang; Ward, Kevin R.; Pfaffenberger, Michael Paul; Najarian, Kayvan
• Other Names: Koch G. Academic Editor.; Ma J. Academic Editor.; Positano V. Academic Editor.
• Is Part Of: TheScientificWorldjournal. Volume 2013(2013)
• Publication Date: 2013-05-20
• Subjects: Science -- Periodicals; Technology -- Periodicals; Medicine -- Periodicals; 505
• DOI: 10.1155/2013/896056
• Languages: English
• ISSNs: 2356-6140

Abstract

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.