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多尺度信息分解长记忆过程:在颅内压高原波中的应用。

Multiscale Information Decomposition of Long Memory Processes: Application to Plateau Waves of Intracranial Pressure.

出版信息

Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:1753-1756. doi: 10.1109/EMBC48229.2022.9870925.

DOI:10.1109/EMBC48229.2022.9870925
PMID:36085854
Abstract

Traumatic Brain Injury (TBI) patients present high levels of physical stress, which in some situations can manifest as Plateau Wave (PW) episodes. This intense stress phenomenon can be evidenced by Heart Rate Variability (HRV). Thus, the multivariate and simultaneous analysis of cardio-cerebrovascular oscillations, involving the RR intervals, mean arterial pressure (MAP) and the amplitude of intracranial pressure (AMP), will be useful to understand the interconnections between body signals, allowing the interpretation of the combined activity of pathophysiological mechanisms. In this work, the multiscale representation of the Transfer Entropy (TE) and of its decomposition in the network of these three interacting processes is obtained, based on a Vector AutoRegressive Fractionally Integrated (VARFI) framework for Gaussian processes. This method allows to assess directed interactions and to quantify the information flow accounting for the simultaneous presence of short-term dynamics and long-range correlations. The results show that the baseline RR, but not MAP can provide information about the possibility of a PW arising. During PW, the long-term correlations highlight synergistic interactions between MAP and AMP processes on RR. The multiscale decomposition of the information along with the incorporation of the long term correlations allowed a better description of HRV during PW, highlighting the fact that the HRV mirrors this cerebrovascular phenomena.

摘要

创伤性脑损伤 (TBI) 患者表现出较高的身体压力,在某些情况下会表现出高原波 (PW) 发作。这种强烈的压力现象可以通过心率变异性 (HRV) 来证明。因此,对涉及 RR 间隔、平均动脉压 (MAP) 和颅内压幅度 (AMP) 的心脏-脑血管波动的多变量和同时分析,将有助于理解身体信号之间的相互关系,从而可以解释病理生理机制的联合活动。在这项工作中,基于高斯过程的向量自回归分数积分 (VARFI) 框架,获得了转移熵 (TE) 的多尺度表示及其在这三个相互作用过程网络中的分解。该方法允许评估有向相互作用,并量化考虑短期动态和长程相关性的同时存在的信息流。结果表明,基线 RR,但不是 MAP,可以提供关于出现 PW 的可能性的信息。在 PW 期间,长期相关性突出了 MAP 和 AMP 过程之间的协同相互作用对 RR 的影响。信息的多尺度分解以及长期相关性的纳入允许更好地描述 PW 期间的 HRV,突出了 HRV 反映这种脑血管现象的事实。

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