Replication Data for: Non-Invasive Monitoring of Microvascular Oxygenation and Reactive Hyperemia using Hybrid, Near-Infrared Diffuse Optical Spectroscopy for Critical Care

In this work, a state-of-the-art bed-side monitor combining time-domain near infrared spectroscopy, diffuse correlation spectroscopy, a pulse oximeter, and an automatized tourniquet was utilized. An automatized vascular occlusion test was used to evaluate peripheral endothelial, microvascular, and metabolic function in a subject admitted to the intensive care (ICU).
The data used in this study are read from binary files produced by the platform.
These binary files follow a pre-determined format, allowing them to be easily read and processed using various platforms such as MATLAB, Python, and R.
The data processing involves then importing the data from these files, followed by appropriate preprocessing steps to ensure appropriate data quality of the displayed data.
Preprocessing involves several key steps to prepare the data for analysis.
First, movement artifacts are removed.
Next, data alignment is performed to synchronize time-series data. We utilize the real time data extracted from the binary data to extract relevant features: simple linear fitting and integration techniques are used to retrieve the deoxygenation and reoxygenation rates during the VOT, along with the area under the curves for tissue oxygenation and blood flow, respectively.

本研究采用一款前沿的床边监护仪，整合了时域近红外光谱法（time-domain near infrared spectroscopy）、漫相关光谱法（diffuse correlation spectroscopy）、脉搏血氧仪与自动化止血带。本研究针对入住重症监护病房（ICU）的受试者，采用自动化血管闭塞试验（vascular occlusion test, VOT）评估其外周内皮、微血管与代谢功能。
本研究使用的数据均源自该平台生成的二进制文件。
上述二进制文件遵循预先设定的格式，可通过MATLAB、Python、R等多种平台轻松读取与处理。
数据处理流程首先从这些文件中导入原始数据，随后执行适配的预处理步骤，以保障展示数据的质量符合要求。
预处理包含若干关键环节，用于为后续分析做好数据准备。
首先移除运动伪影。
随后执行数据对齐操作，以同步各时间序列数据。
我们从二进制数据中提取实时相关特征：分别采用简单线性拟合与积分技术，获取血管闭塞试验期间的脱氧速率与复氧速率，以及组织氧合与血流量对应的曲线下面积。