Cancellation of artifacts in ECG signals using a normalized adaptive neural filter

Yunfeng Wu, Rangaraj M. Rangayyan, Sin Chun Ng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Citations (Scopus)

Abstract

Denoising electrocardiographic (ECG) signals is an essential procedure prior to their analysis. In this paper, we present a normalized adaptive neural filter (NANF) for cancellation of artifacts in ECG signals. The normalized filter coefficients are updated by the steepest-descent algorithm; the adaptation process is designed to minimize the difference between second-order estimated output values and the desired artifact-free ECG signals. Empirical results with benchmark data show that the adaptive artifact canceller that includes the NANF can effectively remove muscle-contraction artifacts and high-frequency noise in ambulatory ECG recordings, leading to a high signal-to-noise ratio. Moreover, the performance of the NANF in terms of the root-mean-squared error, normalized correlation coefficient, and filtered artifact entropy is significantly better than that of the popular least-mean-square (LMS) filter.

Original languageEnglish
Title of host publication29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, EMBC'07
Pages2552-2555
Number of pages4
DOIs
Publication statusPublished - 2007
Event29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, EMBC'07 - Lyon, France
Duration: 23 Aug 200726 Aug 2007

Publication series

NameAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
ISSN (Print)0589-1019

Conference

Conference29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, EMBC'07
Country/TerritoryFrance
CityLyon
Period23/08/0726/08/07

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