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Efficacy of Using Mean Arterial Blood Pressure Sequence for Linear Modeling of Cerebral Autoregulation

机译:使用平均动脉血压序列对脑自动调节进行线性建模的功效

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Linear autoregressive (ARX) models are often used to describe the dynamic cerebral autoregulation in humans by relating cerebral blood flow velocity (CBFV) to beat-to-beat mean arterial blood pressure (MABP). For linear model estimation it is required that the input be persistently exciting. This study aimed to establish if the MABP is adequately persistently exciting for estimating to yield a linear model. Using ARX models with MABP as input and CBFV as output, linear models for 11 healthy normal subjects in supine position were obtained. The order of the models was allowed to vary between 1 to 10. For each subject, the model with the least mean squared error (MSE) value was selected, called Ma. Ma was then treated as the unknown model of the cerebral autoregulation to be estimated. Ma was separately subjected to the measured MABP as well as a pseudo random binary sequence (PRBS) to estimate two ARX models for it. The resulting estimates of Ma with the lowest MSE were selected as Me1 and Me2, respectively. With the measured MABP as input, the MSE values between the resulting output of Me1 and Me2 and the measured CBFV were calculated. These MSE values were compared to the MSE value previously obtained for Ma to determine if Me1 that was obtained using MABP can estimate CBFV with the same level of accuracy as Me2. This analysis was carried out both with the traditional 6 minutes data and was repeated by dividing the 6 minutes of data into four 1.5 minute sections, a total of 5 comparisons. The analysis showed that the computed MSE values for Ma, Me1 and Me2 were the same for each subject, irrespective of the duration of the data set used for the study. However, the orders of the models were not identical. For each of the three models the average MSE value for 11 subjects was 0.0200 for 6 minutes, 0.0235 for first- 1.5 minute and 0.0263, 0.0278 and 0.0255 for second, third and fourth 1.5 minutes, respectively. Results suggest that 1.5 minutes of MABP sequence is adequate as input for estimating linear models of cerebral autoregulation
机译:线性自回归(ARX)模型通常用于通过将脑血流速度(CBFV)与逐搏平均动脉血压(MABP)相关联来描述人体内动态脑自动调节。对于线性模型估计,要求输入必须持续激励。这项研究旨在确定MABP是否足以持续激发估计线性模型。使用以MABP作为输入并以CBFV作为输出的ARX模型,获得了11位仰卧位健康正常受试者的线性模型。模型的顺序允许在1到10之间变化。对于每个对象,选择具有最小均方误差(MSE)值的模型,称为M a 。然后将M a 视为要估计的大脑自动调节的未知模型。对M a 分别进行测量的MABP和伪随机二进制序列(PRBS),以为其估计两个ARX模型。 MSE最低的M a 的最终估计值分别被选为M e1 和M e2 。以测得的MABP作为输入,计算出M e1 和M e2 的最终输出与测得的CBFV之间的MSE值。将这些MSE值与先前为M a 获得的MSE值进行比较,以确定使用MABP获得的M e1 是否可以以与M < sub> e2 。该分析是使用传统的6分钟数据进行的,并且通过将6分钟数据分为四个1.5分钟的部分进行了重复,共进行了5次比较。分析显示,对于每个对象,M a ,M e1 和M e2 的MSE值均相同,而与持续时间无关用于研究的数据集。但是,模型的顺序并不相同。对于这三个模型中的每一个,11位受试者的平均MSE值分别为6分钟为0.0200,前1.5分钟为0.0235,第二,第三和第四1.5分钟分别为0.0263、0.0278和0.0255。结果表明1.5分钟的MABP序列足以作为估计脑自动调节线性模型的输入

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