基于多项式函数的近红外光谱脑血流动力学监测中基线漂移的在线去除
Online Removal of Baseline Shift with a Polynomial Function for Hemodynamic Monitoring Using Near-Infrared Spectroscopy.
机构信息
State Key lab Elect Thin Film & Integrated Device University of Electronic Science and Technology of China, Chengdu 610054, China.
Biomedical Engineering Institute, Chinese Academy of Medical Science and Peking Union Medical College, Tianjin 300192, China.
出版信息
Sensors (Basel). 2018 Jan 21;18(1):312. doi: 10.3390/s18010312.
Near-infrared spectroscopy (NIRS) has become widely accepted as a valuable tool for noninvasively monitoring hemodynamics for clinical and diagnostic purposes. Baseline shift has attracted great attention in the field, but there has been little quantitative study on baseline removal. Here, we aimed to study the baseline characteristics of an in-house-built portable medical NIRS device over a long time (>3.5 h). We found that the measured baselines all formed perfect polynomial functions on phantom tests mimicking human bodies, which were identified by recent NIRS studies. More importantly, our study shows that the fourth-order polynomial function acted to distinguish performance with stable and low-computation-burden fitting calibration (R-square >0.99 for all probes) among second- to sixth-order polynomials, evaluated by the parameters R-square, sum of squares due to error, and residual. This study provides a straightforward, efficient, and quantitatively evaluated solution for online baseline removal for hemodynamic monitoring using NIRS devices.
近红外光谱（NIRS）已被广泛认可为一种有价值的工具，可用于临床和诊断目的的非侵入性监测血液动力学。基线漂移在该领域引起了极大的关注，但对基线去除的定量研究却很少。在这里，我们旨在研究一种内部构建的便携式医疗 NIRS 设备在长时间（>3.5 小时）内的基线特征。我们发现，在模拟人体的体模测试中，所有测量的基线都形成了完美的多项式函数，这与最近的 NIRS 研究一致。更重要的是，我们的研究表明，在二阶到六阶多项式中，四阶多项式函数可以通过参数 R 平方、误差平方和和残差来区分性能，四阶多项式函数具有稳定和低计算负担的拟合校准（所有探头的 R 平方>0.99）。这项研究为使用 NIRS 设备进行血液动力学监测的在线基线去除提供了一种简单、高效和定量评估的解决方案。
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