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Burst suppression probability algorithms: state-space methods for tracking EEG burst suppression

机译:突发抑制概率算法:用于跟踪EEG突发抑制的状态空间方法

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摘要

Objective. Burst suppression is an electroencephalogram pattern in which bursts of electrical activity alternate with an isoelectric state. This pattern is commonly seen in states of severely reduced brain activity such as profound general anesthesia, anoxic brain injuries, hypothermia and certain developmental disorders. Devising accurate, reliable ways to quantify burst suppression is an important clinical and research problem. Although thresholding and segmentation algorithms readily identify burst suppression periods, analysis algorithms require long intervals of data to characterize burst suppression at a given time and provide no framework for statistical inference. Approach. We introduce the concept of the burst suppression probability (BSP) to define the brain's instantaneous propensity of being in the suppressed state. To conduct dynamic analyses of burst suppression we propose a state-space model in which the observation process is a binomial model and the state equation is a Gaussian random walk. We estimate the model using an approximate expectation maximization algorithm and illustrate its application in the analysis of rodent burst suppression recordings under general anesthesia and a patient during induction of controlled hypothermia. Main result. The BSP algorithms track burst suppression on a second-to-second time scale, and make possible formal statistical comparisons of burst suppression at different times. Significance. The state-space approach suggests a principled and informative way to analyze burst suppression that can be used to monitor, and eventually to control, the brain states of patients in the operating room and in the intensive care unit.
机译:目的。猝发抑制是一种脑电图模式,其中电活动的猝发与等电状态交替出现。这种模式通常出现在大脑活动严重减少的状态中,例如深度全身麻醉,缺氧性脑损伤,体温过低和某些发育障碍。设计准确,可靠的量化突波抑制的方法是一个重要的临床和研究问题。尽管阈值化和分段算法很容易识别突发抑制周期,但是分析算法需要较长的数据间隔来表征给定时间的突发抑制,并且不提供统计推断的框架。方法。我们引入突发抑制概率(BSP)的概念,以定义大脑处于抑制状态的瞬时倾向。为了进行突发抑制的动态分析,我们提出了一个状态空间模型,其中观察过程是一个二项式模型,状态方程是一个高斯随机游动。我们使用近似期望最大化算法估计该模型,并说明其在全麻和诱导受控低温过程中的患者对啮齿动物爆发抑制记录的分析中的应用。主要结果。 BSP算法在秒到秒的时间范围内跟踪突发抑制,并使在不同时间进行突发抑制的正式统计比较成为可能。意义。状态空间方法提出了一种分析突发抑制的原理性和信息性方法,该方法可用于监视并最终控制手术室和重症监护室中患者的大脑状态。

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