Morphological clustering and analysis of continuous intracranial pressure.
Morphological clustering and analysis of continuous intracranial pressure.
复制标题
连续颅内压的形态聚类与分析
DOI：
10.1109/tbme.2008.2008636
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发表时间：
2009-03
期刊：
影响因子：
--
通讯作者：
Bergsneider M
中科院分区：
文献类型：
--
作者：
Hu X;Xu P;Scalzo F;Vespa P;Bergsneider M
颅内压（ICP）的连续测量是一个重要的和既定的临床工具，用于管理许多神经外科疾病，如创伤性脑损伤。目前在流行的临床实践中仅使用平均ICP信息，忽略了可以连续采集的ICP脉冲波形中的有用信息，这些信息可能用于预测颅内和脑血管病理生理变化。本研究介绍并验证了一种算法进行自动分析的连续ICP脉冲波形。该算法能够提高ICP信号质量，识别非人为ICP脉冲，并最佳地指定ICP脉冲中的三个公认的子分量。所提出的算法的验证是通过比较非人为脉冲识别和峰值指定结果从人类观察者与自动分析的基础上建立从700小时的记录从66名神经外科患者的大信号数据库。识别非伪影ICP脉冲的准确率达到97.84%。三个确定的ICP亚峰的准确度分别为90.17%、87.56%和86.53%。这些结果表明，所提出的算法可以可靠地应用于处理来自真实的临床环境的连续ICP记录，以提取有用的ICP脉冲的形态特征。
The continuous measurement of intracranial pressure (ICP) is an important and established clinical tool that is used in the management of many neurosurgical disorders such as traumatic brain injury. Only mean ICP information is used currently in the prevailing clinical practice, ignoring the useful information in ICP pulse waveform that can be continuously acquired and is potentially useful for forecasting intracranial and cerebrovascular pathophysiological changes. The present study introduces and validates an algorithm of performing automated analysis of continuous ICP pulse waveform. This algorithm is capable of enhancing ICP signal quality, recognizing non artifactual ICP pulses, and optimally designating the three well-established subcomponents in an ICP pulse. Validation of the proposed algorithm is done by comparing non artifactual pulse recognition and peak designation results from a human observer with those from automated analysis based on a large signal database built from 700 h of recordings from 66 neurosurgical patients. An accuracy of 97.84% is achieved in recognizing non artifactual ICP pulses. An accuracy of 90.17%, 87.56%, and 86.53% was obtained for designating each of the three established ICP subpeaks. These results show that the proposed algorithm can be reliably applied to process continuous ICP recordings from real clinical environment to extract useful morphological features of ICP pulses.