Guarini M, Urzúa J, Cipriano A, González W
Department of Electrical Engineering, Catholic University of Chile, Chile.
IEEE Trans Biomed Eng. 1998 Dec;45(12):1420-8. doi: 10.1109/10.730436.
This paper presents a method for estimating parameters of a cardiovascular model, including the left-ventricular function, using the sequential quadratic programming (SQP) and the least minimum square (LMS) algorithms. In a first stage, a radial arterial-pressure waveform with corresponding cardiac output are used to automatically seek the set of parameters of the diastolic model. Computer simulation of the model using these parameters generate a pressure waveform and a cardiac output very close to those used for the estimation. In a second stage, the estimated arterial load parameters are used to select the best left-ventricular model function, from four different possibilities, and to estimate its optimum parameter values. The method has been tested numerically and applied to real cases, using data obtained from cardiovascular patients. It has also been subjected to preliminary validation using data obtained from laboratory dogs, in which cardiovascular function was artificially altered.
本文提出了一种使用序列二次规划（SQP）和最小均方（LMS）算法来估计心血管模型参数的方法，该模型包括左心室功能。在第一阶段，利用带有相应心输出量的桡动脉压力波形自动寻找舒张期模型的参数集。使用这些参数对模型进行计算机模拟，生成的压力波形和心输出量与用于估计的波形和心输出量非常接近。在第二阶段，利用估计出的动脉负荷参数从四种不同可能性中选择最佳的左心室模型函数，并估计其最佳参数值。该方法已通过数值测试，并应用于实际病例，使用的是从心血管疾病患者获得的数据。它还使用从实验室犬获得的数据进行了初步验证，在这些犬中，心血管功能被人为改变。
