The Use of Ensemble Empirical Mode Decomposition With Canonical Correlation Analysis as a Novel Artifact Removal Technique
The Use of Ensemble Empirical Mode Decomposition With Canonical Correlation Analysis as a Novel Artifact Removal Technique
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DOI：
10.1109/tbme.2012.2225427
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发表时间：
2013-01-01
影响因子：
4.6
通讯作者：
Ward, Tomas E.
中科院分区：
文献类型：
--
作者：
Sweeney, Kevin T.;McLoone, Sean F.;Ward, Tomas E.
生物信号测量和处理越来越多地部署在流动环境中，特别是在互联健康应用中。这种环境极大地增加了伪影的可能性，伪影可能会遮挡感兴趣的特征并降低信号中可用信息的质量。如果多通道录音可用于给定的信号源，那么目前有相当多的方法可以抑制或在某些情况下消除此类伪影的失真效果。然而，如果只有单通道测量可用，则可用的技术要少得多，但在需要最小仪器复杂性的情况下，单通道测量很重要。本文描述了一种在这种情况下使用的新颖的伪影去除技术。称为集成经验模态分解与典型相关分析 (EEMD-CCA) 的技术能够在单通道测量上运行。 EEMD技术首先用于将单通道信号分解为多维信号。然后采用 CCA 技术使用二阶统计将伪影分量与底层信号隔离。该新技术使用脑电图和功能性近红外光谱数据对当前可用的小波去噪和 EEMD-ICA 技术进行了测试，结果显示可产生显着改善的结果。
Biosignal measurement and processing is increasingly being deployed in ambulatory situations particularly in connected health applications. Such an environment dramatically increases the likelihood of artifacts which can occlude features of interest and reduce the quality of information available in the signal. If multichannel recordings are available for a given signal source, then there are currently a considerable range of methods which can suppress or in some cases remove the distorting effect of such artifacts. There are, however, considerably fewer techniques available if only a single-channel measurement is available and yet single-channel measurements are important where minimal instrumentation complexity is required. This paper describes a novel artifact removal technique for use in such a context. The technique known as ensemble empirical mode decomposition with canonical correlation analysis (EEMD-CCA) is capable of operating on single-channel measurements. The EEMD technique is first used to decompose the single-channel signal into a multidimensional signal. The CCA technique is then employed to isolate the artifact components from the underlying signal using second-order statistics. The new technique is tested against the currently available wavelet denoising and EEMD-ICA techniques using both electroencephalography and functional near-infrared spectroscopy data and is shown to produce significantly improved results.