A Methodology for Validating Artifact Removal Techniques for Physiological Signals

Abstract

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.

The Use of Ensemble Empirical Mode Decomposition With Canonical Correlation Analysis as a Novel Artifact Removal Technique

“…The data for both modalities was recorded independently using the novel recording methodology proposed in [25]. This particular recording methodology is capable of producing two highly correlated signals for each modality.…”

Functional near-infrared spectroscopy-based correlates of prefrontal cortical dynamics during a cognitive-motor executive adaptation task

“…While EEG provides a measure of neural electrical activity, by contrast fNIR measures blood oxygenation levels via infrared light (e.g., Izzetoglu et al, 2007; Ayaz et al, 2009, 2011, 2012a,b,c). In essence, fNIR can provide different and complementary biological markers for brain dynamics with increased robustness to artifacts during cognitive and motor performance under everyday conditions and in real life environments (e.g., Coyle et al, 2007; Hatakenaka et al, 2007; Leff et al, 2008a,b; Abdelnour and Huppert, 2009; Ayaz et al, 2009, 2011, 2012a,b,c; Gentili et al, 2010a; James et al, 2010, 2012; Power et al, 2012; Sweeney et al, 2012). Comparatively, it was demonstrated that fNIR could indicate various levels of cognitive workload (Izzetoglu et al, 2005; Ayaz et al, 2009, 2012a,b,c; James et al, 2012; Power et al, 2012) as well as changes in motor performance (Hatakenaka et al, 2007; Ikegami and Taga, 2008; Leff et al, 2008a,b; Morihiro et al, 2009; Gentili et al, 2010a).…”

A Real-Time Kinect Signature-Based Patient Home Monitoring System

“…More enhanced algorithms can aggregate to the quality measurement higher statistical moments, or abnormal skewness or Kurtosis values [53]. In addition, the artifact detection algorithm can incorporate knowledge about invalid postures, using a priori constraints of the human body [54], about the operation range (if it resides in the optimal coverage), and about the features that are derived by interpolation, mainly when parts of the human body are out of the Kinect frame.…”