Methodology for Validating Artifact Removal Techniques for Physiological Signals

Kevin Sweeney, Hasan Ayaz, Tomas Ward, Meltem Izzetoglu, Sean McLoone, Banu Onaral

Research output: Contribution to journalArticle

52 Citations (Scopus)

Abstract

Artifact removal from physiological signals is an essential component of the biosignal processing pipeline. The need for powerful and robust methods for this process has become particularly acute as healthcare technology deployment undergoes transition from the current hospital-centric setting toward a wearable and ubiquitous monitoring environment. Currently, determining the relative efficacy and performance of the multiple artifact removal techniques available on real world data can be problematic, due to incomplete information on the uncorrupted desired signal. The majority of techniques are presently evaluated using simulated data, and therefore, the quality of the conclusions is contingent on the fidelity of the model used. Consequently, in the biomedical signal processing community, there is considerable focus on the generation and validation of appropriate signal models for use in artifact suppression. Most approaches rely on mathematical models which capture suitable approximations to the signal dynamics or underlying physiology and, therefore, introduce some uncertainty to subsequent predictions of algorithm performance. This paper describes a more empirical approach to the modeling of the desired signal that we demonstrate for functional brain monitoring tasks which allows for the procurement of a ground truth signal which is highly correlated to a true desired signal that has been contaminated with artifacts. The availability of this ground truth, together with the corrupted signal, can then aid in determining the efficacy of selected artifact removal techniques. A number of commonly implemented artifact removal techniques were evaluated using the described methodology to validate the proposed novel test platform. © 2012 IEEE.
Original languageEnglish
Article number6236173
Pages (from-to)918-926
Number of pages9
JournalIEEE Transactions on Information Technology in Biomedicine
Volume16
Issue number5
DOIs
Publication statusPublished - Sep 2012

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Biotechnology
  • Computer Science Applications

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