PDF(Pubmed)
Abstract：
OBJECTIVE: This study aims to describe the total EEG energy during episodes of intracranial hypertension (IH) and evaluate its potential as a classification feature for IH.
METHODS: We computed the sample correlation coefficient between intracranial pressure (ICP) and the total EEG energy. Additionally, a generalized additive model was employed to assess the relationship between arterial blood pressure (ABP), total EEG energy, and the odds of IH.
RESULTS: The median sample cross-correlation between total EEG energy and ICP was 0.7, and for cerebral perfusion pressure (CPP) was 0.55. Moreover, the proposed model exhibited an accuracy of 0.70, sensitivity of 0.53, specificity of 0.79, precision of 0.54, F1-score of 0.54, and an AUC of 0.7.
METHODS: The only existing comparable methods, up to our knowledge, use 13 variables as predictor of IH, our model uses only 3, our model, as it is an extension of the generalized model is interpretable and it achieves the same performance.
CONCLUSIONS: These findings hold promise for the advancement of multimodal monitoring systems in neurocritical care and the development of a non-invasive ICP monitoring tool, particularly in resource-constrained environments.
METHODS: We computed the sample correlation coefficient between intracranial pressure (ICP) and the total EEG energy. Additionally, a generalized additive model was employed to assess the relationship between arterial blood pressure (ABP), total EEG energy, and the odds of IH.
RESULTS: The median sample cross-correlation between total EEG energy and ICP was 0.7, and for cerebral perfusion pressure (CPP) was 0.55. Moreover, the proposed model exhibited an accuracy of 0.70, sensitivity of 0.53, specificity of 0.79, precision of 0.54, F1-score of 0.54, and an AUC of 0.7.
METHODS: The only existing comparable methods, up to our knowledge, use 13 variables as predictor of IH, our model uses only 3, our model, as it is an extension of the generalized model is interpretable and it achieves the same performance.
CONCLUSIONS: These findings hold promise for the advancement of multimodal monitoring systems in neurocritical care and the development of a non-invasive ICP monitoring tool, particularly in resource-constrained environments.
摘要：
目的：本研究旨在描述颅内高压（IH）发作期间的总EEG能量，并评估其作为IH分类特征的潜力。
方法：我们计算了颅内压（ICP）与总EEG能量之间的样本相关系数。此外,采用广义相加模型来评估动脉血压(ABP)与动脉血压之间的关系，总脑电图能量，和IH的几率。
结果：总EEG能量与ICP之间的样本交叉相关性中位数为0.7，脑灌注压（CPP）为0.55。此外，该模型的准确度为0.70,灵敏度为0.53,特异性为0.79,精密度为0.54,F1评分为0.54,AUC为0.7.
方法：唯一现有的可比方法，根据我们的知识，使用13个变量作为IH的预测因子，我们的模型只使用3，我们的模型，因为它是广义模型的扩展是可解释的，并且实现了相同的性能。
结论：这些发现为神经重症监护中的多模式监测系统的发展和非侵入性ICP监测工具的开发提供了希望。特别是在资源受限的环境中。
方法：我们计算了颅内压（ICP）与总EEG能量之间的样本相关系数。此外,采用广义相加模型来评估动脉血压(ABP)与动脉血压之间的关系，总脑电图能量，和IH的几率。
结果：总EEG能量与ICP之间的样本交叉相关性中位数为0.7，脑灌注压（CPP）为0.55。此外，该模型的准确度为0.70,灵敏度为0.53,特异性为0.79,精密度为0.54,F1评分为0.54,AUC为0.7.
方法：唯一现有的可比方法，根据我们的知识，使用13个变量作为IH的预测因子，我们的模型只使用3，我们的模型，因为它是广义模型的扩展是可解释的，并且实现了相同的性能。
结论：这些发现为神经重症监护中的多模式监测系统的发展和非侵入性ICP监测工具的开发提供了希望。特别是在资源受限的环境中。
