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    A Methodology for Validating Artifact Removal Techniques for Physiological Signals


    Sweeney, Kevin and Ayaz, Hasan and Ward, Tomas E. and Izzetoglu, Meltem and McLoone, Sean F. and Onaral, Banu (2012) A Methodology for Validating Artifact Removal Techniques for Physiological Signals. IEEE Transactions on Information Technology in Biomedicine, 16 (5). pp. 918-926. ISSN 1089-7771

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    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.

    Item Type: Article
    Keywords: Artifact removal; electroencephalography (EEG); functional Near-Infrared Spectroscopy (fNIRS); recording methodology;
    Academic Unit: Faculty of Science and Engineering > Electronic Engineering
    Item ID: 4161
    Depositing User: Sean McLoone
    Date Deposited: 31 Jan 2013 12:36
    Journal or Publication Title: IEEE Transactions on Information Technology in Biomedicine
    Publisher: IEEE
    Refereed: Yes
    URI:

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