Aalto University School of Science, Department of Biomedical Engineering and Computational Science, P.O. Box 12200, FI-00076 Aalto, Finland.
J Biomed Opt. 2011 Aug;16(8):087005. doi: 10.1117/1.3606576.
In medical near-infrared spectroscopy (NIRS), movements of the subject often cause large step changes in the baselines of the measured light attenuation signals. This prevents comparison of hemoglobin concentration levels before and after movement. We present an accelerometer-based motion artifact removal (ABAMAR) algorithm for correcting such baseline motion artifacts (BMAs). ABAMAR can be easily adapted to various long-term monitoring applications of NIRS. We applied ABAMAR to NIRS data collected in 23 all-night sleep measurements and containing BMAs from involuntary movements during sleep. For reference, three NIRS researchers independently identified BMAs from the data. To determine whether the use of an accelerometer improves BMA detection accuracy, we compared ABAMAR to motion detection based on peaks in the moving standard deviation (SD) of NIRS data. The number of BMAs identified by ABAMAR was similar to the number detected by the humans, and 79% of the artifacts identified by ABAMAR were confirmed by at least two humans. While the moving SD of NIRS data could also be used for motion detection, on average 2 out of the 10 largest SD peaks in NIRS data each night occurred without the presence of movement. Thus, using an accelerometer improves BMA detection accuracy in NIRS.
在医学近红外光谱（NIRS）中，由于被试者的运动，导致测量光衰减信号的基线发生大的阶跃变化。这使得运动前后的血红蛋白浓度水平无法进行比较。我们提出了一种基于加速度计的运动伪影去除（ABAMAR）算法，用于校正这种基线运动伪影（BMA）。ABAMAR 可以很容易地适应各种 NIRS 的长期监测应用。我们将 ABAMAR 应用于在 23 次整晚睡眠测量中采集的 NIRS 数据，这些数据包含了睡眠期间无意识运动引起的 BMA。为了参考，三位 NIRS 研究人员分别独立地从数据中识别 BMA。为了确定使用加速度计是否可以提高 BMA 检测准确性，我们将 ABAMAR 与基于 NIRS 数据移动标准差（SD）峰值的运动检测进行了比较。ABAMAR 识别的 BMA 数量与人类检测到的数量相似，ABAMAR 识别的 79%的伪影至少被两个人类确认。虽然 NIRS 数据的移动 SD 也可用于运动检测，但平均每晚 NIRS 数据中 10 个最大 SD 峰值中有 2 个峰值没有出现运动。因此，在 NIRS 中使用加速度计可以提高 BMA 检测准确性。
