Scalzo Fabien, Bergsneider Marvin, Vespa Paul M, Martin Neil A, Hu Xiao
Department of Neurosurgery, Geffen School of Medicine, University of California, Los Angeles, USA.
IEEE Pulse. 2012 Mar;3(2):49-52. doi: 10.1109/MPUL.2011.2181024.
The waveform morphology of intracranial pressure (ICP) pulses holds essential information about intracranial and cerebrovascular pathophysiologies. Automatic analysis of the ICP waveforms may help to predict abnormal increase of ICP and thus prevent severe complications in patients treated for traumatic brain injuries (TBIs). This article describes a probabilistic framework to track the ICP waveform morphology in real time. The model represents the correlation between different ICP morphological metrics extracted within a single pulse as well as the temporal dependence of metrics extracted between successive pulses. Morphological tracking is solved using Bayesian inference in a dynamic graphical model that associates a random variable to each morphological metric.
颅内压（ICP）脉搏的波形形态包含有关颅内和脑血管病理生理学的重要信息。对ICP波形进行自动分析可能有助于预测ICP的异常升高，从而预防创伤性脑损伤（TBI）患者出现严重并发症。本文描述了一种用于实时跟踪ICP波形形态的概率框架。该模型表示在单个脉搏内提取的不同ICP形态学指标之间的相关性，以及在连续脉搏之间提取的指标的时间依赖性。形态学跟踪是在一个动态图形模型中使用贝叶斯推理来解决的，该模型将一个随机变量与每个形态学指标相关联。
