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颅内压变化的无创监测:健康志愿者研究。

Non-invasive monitoring of intracranial pressure changes: healthy volunteers study.

作者信息

Roldan Maria, Bradley George R E, Mejía-Mejía Elisa, Abay Tomas Y, Kyriacou Panayiotis A

机构信息

Research Centre for Biomedical Engineering, City University of London, London, United Kingdom.

出版信息

Front Physiol. 2023 Aug 8;14:1208010. doi: 10.3389/fphys.2023.1208010. eCollection 2023.

Abstract

This research aims to evaluate the possible association between pulsatile near infrared spectroscopic waveform features and induced changes in intracranial pressure in healthy volunteers. An optical intracranial pressure sensor was attached to the forehead of 16 healthy volunteers. Pulsatile near infrared spectroscopic signals were acquired from the forehead during body position changes and Valsalva manoeuvers. Features were extracted from the pulsatile signals and analyses were carried out to investigate the presence of statistical differences in the features when intracranial pressure changes were induced. Classification models were developed utilizing the features extracted from the pulsatile near-infrared spectroscopic signals to classify between different body positions and Valsalva manoeuvre. The presence of significant differences in the majority of the analyzed features (p 0.05) indicates the technique's ability to distinguish between variations in intracranial pressure. Furthermore, the disparities observed in the optical signal features captured by the proximal and distal photodetectors support the hypothesis that alterations in back-scattered light directly correspond to brain-related changes. Further research is required to subtract distal and proximal signals and construct predictive models employing a gold standard measurement for non-invasive, continuous monitoring of intracranial pressure. The study investigated the use of pulsatile near infrared spectroscopic signals to detect changes in intracranial pressure in healthy volunteers. The results revealed significant differences in the features extracted from these signals, demonstrating a correlation with ICP changes induced by positional changes and Valsalva manoeuvre. Classification models were capable of identifying changes in ICP using features from optical signals from the brain, with a sensitivity ranging from 63.07% to 80% and specificity ranging from 60.23% to 70% respectively. These findings underscored the potential of these features to effectively identify alterations in ICP. The study's results demonstrate the feasibility of using features extracted from optical signals from the brain to detect changes in ICP induced by positional changes and Valsalva manoeuvre in healthy volunteers. This represents a first step towards the non-invasive monitoring of intracranial pressure.

摘要

本研究旨在评估健康志愿者中脉动近红外光谱波形特征与颅内压诱导变化之间的可能关联。将光学颅内压传感器附着于16名健康志愿者的前额。在体位改变和瓦尔萨尔瓦动作期间从前额采集脉动近红外光谱信号。从脉动信号中提取特征,并进行分析以研究在诱导颅内压变化时特征中是否存在统计学差异。利用从脉动近红外光谱信号中提取的特征开发分类模型,以区分不同的体位和瓦尔萨尔瓦动作。大多数分析特征存在显著差异(p<0.05)表明该技术能够区分颅内压的变化。此外,近端和远端光电探测器捕获的光信号特征中观察到的差异支持了反向散射光的变化直接对应于脑相关变化的假设。需要进一步研究以减去远端和近端信号,并构建采用金标准测量的预测模型,用于颅内压的无创连续监测。该研究调查了使用脉动近红外光谱信号检测健康志愿者颅内压变化的情况。结果显示从这些信号中提取的特征存在显著差异,表明与体位变化和瓦尔萨尔瓦动作诱导的颅内压变化相关。分类模型能够使用来自大脑的光信号特征识别颅内压变化,灵敏度范围为63.07%至80%,特异性范围分别为60.23%至70%。这些发现强调了这些特征有效识别颅内压变化的潜力。该研究结果证明了使用从大脑光信号中提取的特征检测健康志愿者体位变化和瓦尔萨尔瓦动作诱导的颅内压变化的可行性。这代表了朝着颅内压无创监测迈出的第一步。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b47/10443643/61f70f85c71c/fphys-14-1208010-g001.jpg

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