A hidden semi-Markov model for estimating burst suppression EEG.
A hidden semi-Markov model for estimating burst suppression EEG.
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DOI：
10.1109/embc.2019.8856802
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发表时间：
2019-07
期刊：
影响因子：
--
通讯作者：
Brown EN
中科院分区：
文献类型：
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作者：
Chakravarty S;Baum TE;An J;Kahali P;Brown EN
爆发抑制是一种脑电图（EEG）模式，与以大脑代谢抑制为特征的深度失活脑状态相关。这种模式的特点是在相对接近等电位的时期（抑制期）之间穿插着短时间的限带电活动（爆发）。神经生理学的先前研究表明，爆发段和抑制段分别与皮质网络中三磷酸腺苷资源的消耗和再生有关。这表明一旦抑制（或爆发）段开始，随着处于该状态的时间增加，脱离该状态的倾向逐渐增加。先前通过跟踪抑制期相对于爆发期所估计的时间比例来在爆发抑制期间跟踪脑状态的脑电图监测框架没有纳入这一信息。在这项工作中，我们将这一信息纳入一个隐半马尔可夫模型（HSMM）中，其中两个状态（爆发和抑制）使用依赖于逗留时间的转移概率在彼此之间随机转换。我们通过估计状态概率、最优状态序列以及由控制转移概率中逗留时间依赖性的参数所表征的大脑代谢激活水平，展示了HSMM在分析临床数据方面的效用。这里提出的基于HSMM的方法提供了一个新的统计框架，推进了爆发抑制脑电图分析的现有技术水平。
Burst suppression is an electroencephalogram (EEG) pattern associated with profoundly inactivated brain states characterized by cerebral metabolic depression. This pattern is distinguished by short-duration band-limited electrical activity (bursts) interspersed between relatively near-isoelectric periods (suppressions). Prior work in neurophysiology suggests that burst and suppression segments are respectively associated with consumption and regeneration of adenosine triphosphate resource in cortical networks. This indicates that once a suppression (or, burst) segment begins, the propensity to switch out of the state gradually increases with duration spent in the state. Prior EEG monitoring frameworks that track the brain state during burst suppression by tracking the estimated fraction of time spent in suppression, relative to bursts, do not incorporate this information. In this work, we incorporate this information within a hidden semi-Markov model (HSMM) wherein two states (burst & suppression) stochastically switch between each other using sojourn-time dependent transition probabilities. We demonstrate the HSMM’s utility in analyzing clinical data by estimating the state probabilities, the optimal state sequence, and the brain’s metabolic activation level characterized by parameters governing sojourn-time dependence in transition probabilities. The HSMM-based approach proposed here provides a novel statistical framework that advances the state-of-the-art in analyzing burst suppression EEG.