Sweeney Kevin T, Ayaz Hasan, Ward Tomás E, Izzetoglu Meltem, McLoone Seán F, Onaral Banu
Department of Electronic Engineering, National University of Ireland, Maynooth, Ireland.
IEEE Trans Inf Technol Biomed. 2012 Sep;16(5):918-26. doi: 10.1109/TITB.2012.2207400. Epub 2012 Jul 10.
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.
从生理信号中去除伪迹是生物信号处理流程的一个重要组成部分。随着医疗技术的部署从当前以医院为中心的环境向可穿戴和无处不在的监测环境转变，对用于此过程的强大且稳健方法的需求变得尤为迫切。目前，由于关于未受干扰的期望信号的信息不完整，确定现有多种伪迹去除技术在真实世界数据上的相对功效和性能可能存在问题。目前大多数技术是使用模拟数据进行评估的，因此，结论的质量取决于所使用模型的保真度。因此，在生物医学信号处理领域，相当关注用于伪迹抑制的合适信号模型的生成和验证。大多数方法依赖于数学模型，这些模型捕获对信号动态或潜在生理状态的适当近似，因此，会给算法性能的后续预测带来一些不确定性。本文描述了一种针对期望信号建模的更具实证性的方法，我们在功能性脑监测任务中展示了该方法，它允许获取与已被伪迹污染的真实期望信号高度相关的“地面真值”信号。然后，这种“地面真值”信号与受干扰信号一起可有助于确定所选伪迹去除技术的功效。使用所描述的方法评估了一些常用的伪迹去除技术，以验证所提出的新型测试平台。
