Islam Abrar, Sainbhi Amanjyot Singh, Stein Kevin Y, Vakitbilir Nuray, Gomez Alwyn, Silvaggio Noah, Bergmann Tobias, Hayat Mansoor, Froese Logan, Zeiler Frederick A
Department of Biomedical Engineering, Price Faculty of Engineering, University of Manitoba, Winnipeg, MB R3T 2N2, Canada.
Undergraduate Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3T 2N2, Canada.
Sensors (Basel). 2025 Jan 20;25(2):586. doi: 10.3390/s25020586.
Current methodologies for assessing cerebral compliance using pressure sensor technologies are prone to errors and issues with inter- and intra-observer consistency. RAP, a metric for measuring intracranial compensatory reserve (and therefore compliance), holds promise. It is derived using the moving correlation between intracranial pressure (ICP) and the pulse amplitude of ICP (AMP). RAP remains largely unexplored in cases of moderate to severe acute traumatic neural injury (also known as traumatic brain injury (TBI)). The goal of this work is to explore the general description of (a) RAP signal patterns and behaviors derived from ICP pressure transducers, (b) temporal statistical relationships, and (c) the characterization of the artifact profile.
Different summary and statistical measurements were used to describe RAP's pattern and behaviors, along with performing sub-group analyses. The autoregressive integrated moving average (ARIMA) model was employed to outline the time-series structure of RAP across different temporal resolutions using the autoregressive (-order) and moving average orders (-order). After leveraging the time-series structure of RAP, similar methods were applied to ICP and AMP for comparison with RAP. Finally, key features were identified to distinguish artifacts in RAP. This might involve leveraging ICP/AMP signals and statistical structures.
The mean and time spent within the RAP threshold ranges ([0.4, 1], (0, 0.4), and [-1, 0]) indicate that RAP exhibited high positive values, suggesting an impaired compensatory reserve in TBI patients. The median optimal ARIMA model for each resolution and each signal was determined. Autocorrelative function (ACF) and partial ACF (PACF) plots of residuals verified the adequacy of these median optimal ARIMA models. The median of residuals indicates that ARIMA performed better with the higher-resolution data. To identify artifacts, (a) ICP -order, AMP -order, and RAP -order and -order, (b) residuals of ICP, AMP, and RAP, and (c) cross-correlation between residuals of RAP and AMP proved to be useful at the minute-by-minute resolution, whereas, for the 10-min-by-10-min data resolution, only the -order of the optimal ARIMA model of ICP and AMP served as a distinguishing factor.
RAP signals derived from ICP pressure sensor technology displayed reproducible behaviors across this population of TBI patients. ARIMA modeling at the higher resolution provided comparatively strong accuracy, and key features were identified leveraging these models that could identify RAP artifacts. Further research is needed to enhance artifact management and broaden applicability across varied datasets.
目前使用压力传感器技术评估脑顺应性的方法容易出现误差以及观察者间和观察者内一致性方面的问题。RAP是一种用于测量颅内代偿储备（进而测量顺应性）的指标，具有一定前景。它是通过颅内压（ICP）与ICP脉搏振幅（AMP）之间的移动相关性得出的。在中度至重度急性创伤性神经损伤（也称为创伤性脑损伤（TBI））的病例中，RAP在很大程度上仍未得到充分研究。这项工作的目标是探索以下方面的一般描述：（a）从ICP压力传感器得出的RAP信号模式和行为，（b）时间统计关系，以及（c）伪影特征的表征。
使用不同的汇总和统计测量来描述RAP的模式和行为，并进行亚组分析。自回归积分移动平均（ARIMA）模型用于勾勒不同时间分辨率下RAP的时间序列结构，使用自回归（阶数）和移动平均阶数（阶数）。在利用RAP的时间序列结构后，将类似方法应用于ICP和AMP以与RAP进行比较。最后，确定关键特征以区分RAP中的伪影。这可能涉及利用ICP/AMP信号和统计结构。
RAP阈值范围（[0.4, 1]、(0, 0.4)和[-1, 0]）内的平均值和持续时间表明，RAP呈现出高正值，这表明TBI患者的代偿储备受损。确定了每种分辨率和每个信号的中位数最优ARIMA模型。残差的自相关函数（ACF）和偏自相关函数（PACF）图验证了这些中位数最优ARIMA模型的充分性。残差中位数表明，ARIMA在高分辨率数据上表现更好。为了识别伪影，（a）ICP阶数、AMP阶数以及RAP的阶数和阶数，（b）ICP、AMP和RAP的残差，以及（c）RAP和AMP残差之间的互相关在每分钟分辨率下被证明是有用的，而对于每10分钟的数据分辨率，只有ICP和AMP的最优ARIMA模型的阶数作为区分因素。
从ICP压力传感器技术得出的RAP信号在这群TBI患者中表现出可重复的行为。高分辨率下的ARIMA建模提供了相对较强的准确性，并且利用这些模型确定了可以识别RAP伪影的关键特征。需要进一步研究以加强伪影管理并拓宽在各种数据集上的适用性。
