Objective: A noninvasive intracranial pressure (ICP) estimation method is proposed that incorporates a model-based approach within a probabilistic framework to mitigate the effects of data and modeling uncertainties. Methods: A first-order model of the cerebral vasculature relates measured arterial blood pressure (ABP) and cerebral blood flow velocity (CBFV) to ICP. The model is driven by the ABP waveform and is solved for a range of mean ICP values to predict the CBFV waveform. The resulting errors between measured and predicted CBFV are transformed into likelihoods for each candidate ICP in two steps. First, a baseline ICP estimate is established over five data windows of 20 beats by combining the likelihoods with a prior distribution of the ICP to yield an a posteriori distribution whose median is taken as the baseline ICP estimate. A single-state model of cerebral autoregulatory dynamics is then employed in subsequent data windows to track changes in the baseline by combining ICP estimates obtained with a uniform prior belief and model-predicted ICP. For each data window, the estimated model parameters are also used to determine the ICP pulse pressure. Results: On a dataset of thirteen pediatric patients with a variety of pathological conditions requiring invasive ICP monitoring, the method yielded for mean ICP estimation a bias (mean error) of 0.6 mmHg and a root-mean-squared error of 3.7 mmHg. Conclusion: These performance characteristics are well within the acceptable range for clinical decision making. Significance: The method proposed here constitutes a significant step towards robust, continuous, patient-specific noninvasive ICP determination.

中文翻译：

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基于伪贝叶斯模型的无创颅内压估计和跟踪

目标：提出了一种无创颅内压 (ICP) 估计方法，该方法在概率框架内结合了基于模型的方法，以减轻数据和建模不确定性的影响。方法：脑血管系统的一阶模型将测量的动脉血压 (ABP) 和脑血流速度 (CBFV) 与 ICP 联系起来。该模型由 ABP 波形驱动，并求解一系列平均 ICP 值以预测 CBFV 波形。测量的和预测的 CBFV 之间的结果误差通过两个步骤转换为每个候选 ICP 的可能性。首先，通过将似然性与 ICP 的先验分布相结合，在 20 个节拍的五个数据窗口上建立基线 ICP 估计，以产生后验分布，其中值作为基线 ICP 估计。然后在随后的数据窗口中使用大脑自动调节动力学的单状态模型，通过将获得的 ICP 估计与统一先验信念和模型预测的 ICP 相结合来跟踪基线的变化。对于每个数据窗口，估计的模型参数也用于确定 ICP 脉压。结果：在需要有创 ICP 监测的各种病理状况的 13 名儿科患者的数据集上，该方法产生的平均 ICP 估计偏差（平均误差）为 0.6 mmHg，均方根误差为 3.7 mmHg。结论：这些性能特征完全在临床决策的可接受范围内。意义：此处提出的方法是朝着稳健、连续、特定于患者的无创 ICP 测定迈出的重要一步。