ALOPEX优化技术在心血管领域的应用

G.S. Friedrichs , D.S. Berger , E. Micheli-Tzanakou
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引用次数: 2

摘要

ALOPEX是一个包含大量参数的成本函数的一般优化过程,这些参数可以同时调整,直到成本函数达到最优(最大值或最小值);通过在过程中引入随机噪声,避免了局部极值。在本文中,ALOPEX被纳入一个简单的血流动力学研究,其中左心室的电模拟模型被用来建立心肌卒中功的方程。在家兔(n = 5)中进行了初步实验,以评估该优化技术的有效性。在对照状态下,计算出的兔脑卒中功为50±7 mmHg ml,而ALOPEX预测的脑卒中功为51±7 mmHg ml。ALOPEX能够跟踪引入药物后心血管状态的变化。例如,硝普塞治疗后,卒中工作减少了38±6% (P <0.05),而ALOPEX预测较基线下降42±4% (P <0.05)。甲氧苄胺治疗使脑卒中工作增加74±34%,而ALOPEX预测比对照组增加73±43%。计算值与ALOPEX预测值之间无统计学差异。个体模型参数如最大左室弹性(Emax)和左室舒张末期容积(EDV)也可以通过ALOPEX正确预测。我们发现ALOPEX优化技术在预测多参数函数的分量方面是有用的。特别是,我们已经证明它适用于简单的血流动力学模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cardiovascular applications of the ALOPEX optimization technique

ALOPEX is a general optimization process incorporating a cost function containing a large number of parameters which may be simultaneously adjusted until the cost function reaches an optimum (maximum or minimum); local extremes are avoided by introducing random noise into the procedure. In this paper, ALOPEX is incorporated into a simple haemodynamic study in which an electric analogue model of the left ventricle is used to develop equations of myocardial stroke work. Pilot experiments were undertaken in rabbits (n = 5) to gauge the effectiveness of this optimizing technique. In the control state, calculated stroke work for the rabbit was determined to be 50 ± 7 mmHg ml, while ALOPEX predicted a stroke work of 51 ± 7 mmHg ml. ALOPEX is capable of following changing cardiovascular states when pharmacological agents are introduced. For example, after nitroprusside treatment, stroke work was reduced by 38 ± 6% (P < 0.05) while ALOPEX predicted a 42 ± 4% reduction from baseline (P < 0.05). Methoxamine treatment increased stroke work by 74 ± 34%, while ALOPEX predicted a 73 ± 43% increase above control values. There were no statistical differences between calculated and ALOPEX predicted values. Individual model parameters such as maximum left ventricular elastance (Emax) and left ventricular end diastolic volume (EDV) were also predicted correctly by ALOPEX. We have found that the ALOPEX optimization technique is useful in predicting components of multi-parametric functions. In particular, we have shown it to be adaptable to a simple haemodynamic model.

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