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首页> 外文期刊>Journal of Neuroscience Methods >Pattern recognition of overnight intracranial pressure slow waves using morphological features of intracranial pressure pulse.
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Pattern recognition of overnight intracranial pressure slow waves using morphological features of intracranial pressure pulse.

机译：利用颅内压力脉冲的形态学特征识别过夜颅内压力慢波的模式。

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摘要

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%.
机译：这项研究旨在开发一种基于ICP脉冲波形的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）算法用于计算表征ICP脉冲形态的一组度量。使用正则化的线性二次分类器来检验以下假设：ICP慢波和平坦ICP之间的分类可以使用由24个MOCAIP度量的均值和离散组成的特征来实现。为了优化分类性能，应用了三种特征选择技术（差异演化，判别分析和方差分析）来找到不同条件下的最佳MOCAIP度量标准集。此外，我们选择了三组与两种选择方法相结合的度量标准，以用作分类特征（差异演化和方差分析，判别分析和方差分析，以及差异演化和判别分析的组合） 。为了测试该方法，从UCLA成人脑积水中心对44名脑积水患者进行的夜间ICP研究中，获得了总计276种ICP记录选择，分别对应于两种无波动且包含慢波的模式。我们的结果表明，结合使用差分演化和方差分析共同的指标，可以得到最佳的ICP记录（不包含慢波）与ICP的区别。达到89％的准确性，96％的特异性和83％的灵敏度。

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