Neural Systems and Dynamics Laboratory, Department of Neurosurgery, The David Geffen School of Medicine, University of California, CA 90095, Los Angeles, USA.
J Neurosci Methods. 2010 Jul 15;190(2):310-8. doi: 10.1016/j.jneumeth.2010.05.015. Epub 2010 May 26.
This study aimed to develop a new approach to detect intracranial pressure (ICP) slow waves based on morphological changes of ICP pulse waveforms. A recently proposed Morphological Clustering and Analysis of ICP Pulse (MOCAIP) algorithm was utilized to calculate a set of metrics that characterize ICP pulse morphology. A regularized linear quadratic classifier was used to test the hypothesis that classification between ICP slow wave and flat ICP could be achieved using features composed of mean values and dispersion of 24 MOCAIP metrics. To optimize the classification performance, three feature selection techniques (differential evolution, discriminant analysis and analysis of variance) were applied to find an optimal set of MOCAIP metrics under different criteria. In addition, we selected three sets of metrics common to those found by combination of two selection methods, to be used as classification features (differential evolution and analysis of variance, discriminant analysis and analysis of variance, and combination of differential evolution and discriminant analysis). To test the approach, a total of 276 selections of ICP recordings corresponding to two patterns without waves and containing slow waves were obtained from overnight ICP studies of 44 hydrocephalus patients performed at the UCLA Adult Hydrocephalus Center. Our results showed that the best classification performance of differentiation of slow waves from the ICP recording without slow waves was obtained using the combination of metrics common to both differential evolution and analysis of variance methods; achieving an accuracy of 89%, specificity 96%, and sensitivity 83%.
本研究旨在基于颅内压 (ICP) 脉搏波形的形态变化，开发一种新的 ICP 慢波检测方法。利用最近提出的形态聚类和 ICP 脉搏分析 (MOCAIP) 算法来计算一组描述 ICP 脉搏形态的指标。使用正则化线性二次分类器来检验以下假设：使用由 24 个 MOCAIP 指标的平均值和离散度组成的特征，可实现 ICP 慢波与平坦 ICP 之间的分类。为了优化分类性能，应用了三种特征选择技术（差分进化、判别分析和方差分析），根据不同的标准，在不同的标准下找到最佳的 MOCAIP 指标集。此外，我们选择了三个共同的指标集，这些指标集是由两种选择方法组合发现的，作为分类特征（差分进化和方差分析、判别分析和方差分析以及差分进化和判别分析的组合）。为了检验该方法，从加州大学洛杉矶分校成人脑积水中心进行的 44 例脑积水患者的整夜 ICP 研究中获得了总共 276 个对应于无波和包含慢波的两种模式的 ICP 记录的选择。结果表明，使用差分进化和方差分析方法共有的指标组合，可获得最佳的慢波与无慢波 ICP 记录的区分分类性能，准确率为 89%，特异性为 96%，灵敏度为 83%。
