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Parameter Optimization for Selected Correlation Analysis of Intracranial Pathophysiology

机译:颅内病理生理选择相关分析的参数优化

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摘要

Recently we proposed a mathematical tool set, called selected correlation analysis, that reliably detects positive and negative correlations between arterial blood pressure (ABP) and intracranial pressure (ICP). Such correlations are associated with severe impairment of the cerebral autoregulation and intracranial compliance, as predicted by a mathematical model. The time resolved selected correlation analysis is based on a windowing technique combined with Fourier-based coherence calculations and therefore depends on several parameters. For real time application of this method at an ICU it is inevitable to adjust this mathematical tool for high sensitivity and distinct reliability. In this study, we will introduce a method to optimize the parameters of the selected correlation analysis by correlating an index, called selected correlation positive (SCP), with the outcome of the patients represented by the Glasgow Outcome Scale (GOS). For that purpose, the data of twenty-five patients were used to calculate the SCP value for each patient and multitude of feasible parameter sets of the selected correlation analysis. It could be shown that an optimized set of parameters is able to improve the sensitivity of the method by a factor greater than four in comparison to our first analyses.
机译:最近,我们提出了一种数学工具集,称为选定相关分析,可以可靠地检测出动脉血压(ABP)与颅内压(ICP)之间的正相关和负相关。如数学模型所预测的,这种相关性与脑自动调节和颅内顺应性的严重损害相关。时间分辨的选定相关性分析是基于窗口技术与基于傅立叶的相干性计算相结合的,因此取决于几个参数。对于在ICU上实时应用此方法,不可避免地要调整此数学工具以实现高灵敏度和独特的可靠性。在这项研究中,我们将介绍一种通过将称为选定相关正值(SCP)的指数与以格拉斯哥成果量表(GOS)表示的患者结果相关联来优化选定相关性分析参数的方法。为此,使用25位患者的数据来计算每位患者的SCP值以及所选相关性分析的许多可行参数集。可以证明,与我们的最初分析相比,一组优化的参数能够将方法的灵敏度提高四倍以上。

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