脑Willis环集中参数模型的参数识别算法研究
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1.复旦大学航空航天系生物力学研究所;2.复旦大学附属华山医院神经内科

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Research on Parameter Identification Algorithm of Lumped Parameter Model in the Circle of Willis
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    摘要:

    目的:探索三种参数识别方法(阻抗模曲线法、阻抗分量法、遗传算法)在脑Willis环11单元集中参数模型参数识别问题求解上的应用。 方法:以两侧颈内动脉、椎动脉的流量和压力波形为入口条件,计算正常、两侧椎动脉狭窄情况下模型的参数值,使用Simulink建模对识别算法进行验证,最后对流量加一定噪声验证识别算法的稳定性。 结果:正常情况下,阻抗模曲线法获得的近端阻力偏大,阻抗分量法求解的前交通动脉的阻力值及近端阻力值偏大,遗传算法能获得比较合理的模型参数值。两侧椎动脉狭窄情况下,使用阻抗模曲线法能明显得到后循环近端阻力增加的结果,但使用阻抗分量法和遗传算法所得的结果主要是远端阻力有较大增幅。 结论:三种方法识别出来的参数计算出的压力数据和实际数据仍有差别,考虑为建模误差、源数据误差和计算误差。阻抗模曲线法在区分近端阻力变化上有一定效果,但是某些参数的识别上有较大误差。阻抗分量法能够进行参数识别,但方法不稳定计算误差较大。遗传算法能获得比较好的近似解,但在区分椎动脉狭窄上存在一定问题。综合阻抗模曲线法和遗传算法可能在未来使用模型进行疾病诊断上发挥比较好的作用。

    Abstract:

    Objective Explore the application of three parameter identification methods (impedance modulus curve method, impedance component method, and genetic algorithm) in solving the parameter identification problem of the 11-Element lumped parameter model in the circle of Willis. Methods Using the flow and pressure waveforms of the internal carotid arteries and vertebral arteries on both sides as inlet conditions, calculate the parameter values of the model under normal and bilateral vertebral artery stenosis conditions, use Simulink modeling to verify the recognition algorithm, and finally add a certain noise to the flow to verify the stability of the recognition algorithm. Results Under normal circumstances, the proximal resistance obtained by the impedance modulus curve method is too large, and the resistance value of the anterior communicating artery and the proximal resistance value obtained by the impedance component method are too large. The genetic algorithm can obtain relatively reasonable model parameter values. In the case of vertebral artery stenosis on both sides, the impedance modulus curve method can obviously get the result of the increase in the proximal resistance of the posterior circulation, but the result obtained by the impedance component method and the genetic algorithm is mainly that the distal resistance has a larger increase. Conclusions There are still differences between the pressure data calculated by the parameters identified by the above three methods and the actual data, which are considered as modeling errors, source data errors and calculation errors. The impedance modulus curve method has a certain effect in distinguishing the change of the proximal and distal resistance, but there are large errors in the identification of certain parameters. The impedance component method can identify the parameters, but the method is unstable and the calculation error is large. Genetic algorithm can obtain a better approximate solution, but there are certain problems in distinguishing vertebral artery stenosis. The combination impedance model curve method and genetic algorithm may play a better role in the future use of models for disease diagnosis.

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  • 收稿日期:2021-07-16
  • 最后修改日期:2021-07-28
  • 录用日期:2021-08-03
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