School of Biomedical Eng, Science and Health Sys, Drexel University, Philadelphia, PA 19104, USA.
Biomed Eng Online. 2010 Mar 9;9:16. doi: 10.1186/1475-925X-9-16.
As a continuation of our earlier work, we present in this study a Kalman filtering based algorithm for the elimination of motion artifacts present in Near Infrared spectroscopy (NIR) measurements. Functional NIR measurements suffer from head motion especially in real world applications where movement cannot be restricted such as studies involving pilots, children, etc. Since head movement can cause fluctuations unrelated to metabolic changes in the blood due to the cognitive activity, removal of these artifacts from NIR signal is necessary for reliable assessment of cognitive activity in the brain for real life applications.
Previously, we had worked on adaptive and Wiener filtering for the cancellation of motion artifacts in NIR studies. Using the same NIR data set we have collected in our previous work where different speed motion artifacts were induced on the NIR measurements we compared the results of the newly proposed Kalman filtering approach with the results of previously studied adaptive and Wiener filtering methods in terms of gains in signal to noise ratio. Here, comparisons are based on paired t-tests where data from eleven subjects are used.
The preliminary results in this current study revealed that the proposed Kalman filtering method provides better estimates in terms of the gain in signal to noise ratio than the classical adaptive filtering approach without the need for additional sensor measurements and results comparable to Wiener filtering but better suitable for real-time applications.
This paper presented a novel approach based on Kalman filtering for motion artifact removal in NIR recordings. The proposed approach provides a suitable solution to the motion artifact removal problem in NIR studies by combining the advantages of the existing adaptive and Wiener filtering methods in one algorithm which allows efficient real time application with no requirement on additional sensor measurements.
作为我们早期工作的延续，我们在这项研究中提出了一种基于卡尔曼滤波的算法，用于消除近红外光谱（NIR）测量中存在的运动伪影。功能 NIR 测量会受到头部运动的影响，尤其是在无法限制运动的实际应用中，例如涉及飞行员、儿童等的研究。由于头部运动可能会由于认知活动而导致与血液代谢变化无关的波动，因此需要从 NIR 信号中去除这些伪影，以便在实际应用中可靠评估大脑的认知活动。
之前，我们已经研究了自适应和维纳滤波，以消除 NIR 研究中的运动伪影。使用我们在之前的工作中收集的相同的 NIR 数据集，其中在 NIR 测量中引入了不同速度的运动伪影，我们比较了新提出的卡尔曼滤波方法与之前研究的自适应和维纳滤波方法在信噪比增益方面的结果。这里的比较基于配对 t 检验，使用了 11 名受试者的数据。
本研究的初步结果表明，与不需要额外传感器测量的经典自适应滤波方法相比，所提出的卡尔曼滤波方法在信噪比增益方面提供了更好的估计，并且与维纳滤波的结果相当，但更适合实时应用。
本文提出了一种基于卡尔曼滤波的新方法，用于去除 NIR 记录中的运动伪影。该方法通过将现有的自适应和维纳滤波方法的优点结合在一个算法中，为 NIR 研究中的运动伪影去除问题提供了一种合适的解决方案，允许高效的实时应用，而无需额外的传感器测量。
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