Pattern recognition of overnight intracranial pressure slow waves using morphological features of intracranial pressure pulse.
Pattern recognition of overnight intracranial pressure slow waves using morphological features of intracranial pressure pulse.
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DOI：
10.1016/j.jneumeth.2010.05.015
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发表时间：
2010-07-15
影响因子：
3
通讯作者：
Hu, Xiao
中科院分区：
文献类型：
--
作者：
Kasprowicz, Magdalena;Asgari, Shadnaz;Bergsneider, Marvin;Czosnyka, Marek;Hamilton, Robert;Hu, Xiao
本研究旨在开发一种基于 ICP 脉冲波形形态变化检测颅内压 (ICP) 慢波的新方法。最近提出的 ICP 脉冲形态聚类和分析 (MOCAIP) 算法用于计算一组表征 ICP 脉冲形态的指标。使用正则化线性二次分类器来测试以下假设：可以使用由 24 个 MOCAIP 指标的平均值和离散度组成的特征来实现 ICP 慢波和平坦 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）来寻找不同标准下的最佳 MOCAIP 度量集。此外，我们选择了两种选择方法组合所找到的共同的三组度量作为分类特征（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）。为了测试该方法，从加州大学洛杉矶分校成人脑积水中心对 44 名脑积水患者进行的夜间 ICP 研究中获得了总共 276 个 ICP 记录选择，对应于两种无波和包含慢波的模式。我们的结果表明，使用差分进化和方差分析方法通用的度量组合，可以从没有慢波的 ICP 记录中区分出慢波的最佳分类性能；准确度为 89%，特异性为 96%，灵敏度为 83%。
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%.
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DOI：
10.1109/tbme.2008.2008636
发表时间：
2009-03
期刊：
IEEE transactions on bio-medical engineering
影响因子：
--
作者：
Hu X;Xu P;Scalzo F;Vespa P;Bergsneider M
通讯作者：
Bergsneider M
影响因子：
3.2
作者：
Scalzo, Fabien;Xu, Peng;Asgari, Shadnaz;Bergsneider, Marvin;Hu, Xiao
通讯作者：
Hu, Xiao
影响因子：
9.9
作者：
KATZMAN, R;HUSSEY, F
通讯作者：
HUSSEY, F
影响因子：
4.1
作者：
Eklund, A;Ågren-Wilsson, A;Malm, J
通讯作者：
Malm, J
影响因子：
4.2
作者：
Carrera, Emmanuel;Kim, Dong-Joo;Czosnyka, Marek
通讯作者：
Czosnyka, Marek