Neurosurgery Neural Systems and Dynamics Laboratory, Department of Neurosurgery, Geffen School of Medicine, University of California, Los Angeles, USA.
Med Eng Phys. 2012 Oct;34(8):1058-65. doi: 10.1016/j.medengphy.2011.11.010. Epub 2012 Mar 7.
Intracranial pressure (ICP) elevation (intracranial hypertension, IH) in neurocritical care is typically treated in a reactive fashion; it is only delivered after bedside clinicians notice prolonged ICP elevation. A proactive solution is desirable to improve the treatment of intracranial hypertension. Several studies have shown that the waveform morphology of the intracranial pressure pulse holds predictors about future intracranial hypertension and could therefore be used to alert the bedside clinician of a likely occurrence of the elevation in the immediate future. In this paper, a computational framework is proposed to predict prolonged intracranial hypertension based on morphological waveform features computed from the ICP. A key contribution of this work is to exploit an ensemble classifier method based on extremely randomized decision trees (Extra-Trees). Experiments on a representative set of 30 patients admitted for various intracranial pressure related conditions demonstrate the effectiveness of the predicting framework on ICP pulses acquired under clinical conditions and the superior results of the proposed approach in comparison to linear and AdaBoost classifiers.
神经危重症患者的颅内压（ICP）升高（颅内高压，IH）通常采用被动的方式治疗；只有当床边临床医生注意到 ICP 持续升高时才会进行治疗。为了改善颅内高压的治疗，主动式治疗是一种理想的解决方案。多项研究表明，颅内压脉冲的波形形态包含有关未来颅内高压的预测因子，因此可以用于提醒床边临床医生，在不久的将来可能会发生颅内压升高。在本文中，提出了一种基于从 ICP 计算得出的形态波形特征来预测长时间颅内高压的计算框架。这项工作的一个关键贡献是利用基于极端随机决策树（Extra-Trees）的集成分类器方法。在一组具有代表性的 30 名因各种与颅内压相关病症而入院的患者上进行的实验表明，该预测框架在临床条件下获取的 ICP 脉冲上具有有效性，并且与线性和 AdaBoost 分类器相比，所提出的方法具有更优的结果。
