Hu Xiao, Nenov Valeriy, Bergsneider Marvin, Glenn Thomas C, Vespa Paul, Martin Neil
Brain Monitoring and Modeling Laboratory, Division of Neurosurgery, University of California, Los Angeles, CA 90034, USA.
IEEE Trans Biomed Eng. 2007 Apr;54(4):597-610. doi: 10.1109/TBME.2006.890130.
Impeded by the rigid skull, assessment of physiological variables of the intracranial system is difficult. A hidden state estimation approach is used in the present work to facilitate the estimation of unobserved variables from available clinical measurements including intracranial pressure (ICP) and cerebral blood flow velocity (CBFV). The estimation algorithm is based on a modified nonlinear intracranial mathematical model, whose parameters are first identified in an offline stage using a nonlinear optimization paradigm. Following the offline stage, an online filtering process is performed using a nonlinear Kalman filter (KF)-like state estimator that is equipped with a new way of deriving the Kalman gain satisfying the physiological constraints on the state variables. The proposed method is then validated by comparing different state estimation methods and input/output (I/O) configurations using simulated data. It is also applied to a set of CBFV, ICP and arterial blood pressure (ABP) signal segments from brain injury patients. The results indicated that the proposed constrained nonlinear KF achieved the best performance among the evaluated state estimators and that the state estimator combined with the I/O configuration that has ICP as the measured output can potentially be used to estimate CBFV continuously. Finally, the state estimator combined with the I/O configuration that has both ICP and CBFV as outputs can potentially estimate the lumped cerebral arterial radii, which are not measurable in a typical clinical environment.
由于颅骨坚硬，对颅内系统生理变量的评估很困难。在本研究中，采用了一种隐藏状态估计方法，以便从包括颅内压（ICP）和脑血流速度（CBFV）在内的现有临床测量值中估计未观测变量。该估计算法基于一个改进的非线性颅内数学模型，其参数首先在离线阶段使用非线性优化范式进行识别。离线阶段之后，使用一种类似非线性卡尔曼滤波器（KF）的状态估计器进行在线滤波过程，该估计器配备了一种满足状态变量生理约束的卡尔曼增益推导新方法。然后，通过使用模拟数据比较不同的状态估计方法和输入/输出（I/O）配置，对所提出的方法进行验证。它还应用于一组来自脑损伤患者的CBFV、ICP和动脉血压（ABP）信号段。结果表明，所提出的约束非线性KF在评估的状态估计器中表现最佳，并且与以ICP作为测量输出的I/O配置相结合的状态估计器有可能用于连续估计CBFV。最后，与以ICP和CBFV作为输出的I/O配置相结合的状态估计器有可能估计总的脑动脉半径，而这在典型临床环境中是无法测量的。
