Forecasting ICP elevation based on prescient changes of intracranial pressure waveform morphology.
Forecasting ICP elevation based on prescient changes of intracranial pressure waveform morphology.
复制标题
DOI：
10.1109/tbme.2009.2037607
复制
发表时间：
2010-05
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;Asgari S;Vespa P;Bergsneider M
目前只有在医疗保健专业人员注意到持续且显著的平均颅内压升高之后，才会在神经危重护理中实施颅内压升高的干预措施。本研究使用形态聚类和分析颅内压(MOCAIP)算法，得出了24个表征颅内压脉搏形态的指标，并检验了使用这些形态指标可以从与任何颅内压升高无关的对照节段中区分出颅内压升高前(IH前)节段的假设。此外，我们还考察了全局优化算法是否能有效地找到这些形态指标的最优子集，从而获得比使用全集MOCAIP指标更好的分类性能。结果表明，使用差异进化(DE)算法找到的最佳指标子集，IH前节段可以与对照节段区分开来，特异性为97%，敏感性为78%。虽然在颅内压升高前20分钟，对IH前节段的敏感性降至68%，但仍有很高的特异性。结果表明，使用全套MOCAIP指标的绩效不如使用最优指标子集取得的结果。目前的工作表明，先进的颅内压脉冲分析与机器学习相结合，可能会导致对颅内压升高的预测，从而可以在这些准确预测的基础上实现积极的颅内压管理。
Interventions of intracranial pressure (ICP) elevation in neurocritical care is currently delivered only after healthcare professionals notice sustained and significant mean ICP elevation. The present work used the Morphological Clustering and Analysis of Intracranial Pressure (MOCAIP) algorithm to derive 24 metrics characterizing morphology of ICP pulses and tested the hypothesis that pre-intracranial hypertension (pre-IH) segments of ICP can be differentiated, using these morphological metrics, from control segments that were not associated with any ICP elevation. Furthermore, we investigated whether a global optimization algorithm could effectively find the optimal sub-set of these morphological metrics to achieve better classification performance as compared to using full set of MOCAIP metrics. The results showed that Pre-IH segments, using the optimal sub-set of metrics found by the differential evolution (DE) algorithm, can be differentiated from control segments at a specificity of 97% and sensitivity of 78% for those Pre-IH segments 5 minutes prior to the ICP elevation. While the sensitivity decreased to 68% for Pre-IH segments 20 minutes prior to ICP elevation, the high specificity remained. The performance using the full set of MOCAIP metrics was shown inferior to results achieved using the optimal sub-set of metrics. The present work demonstrated that advanced ICP pulse analysis combined with machine learning could potentially lead to the forecasting of ICP elevation so that a proactive ICP management could be realized based on these accurate forecasts.