Research on Parameter Identification Algorithm of Lumped Parameter Model in the Circle of Willis
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    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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History
  • Received:July 16,2021
  • Revised:July 28,2021
  • Adopted:August 03,2021
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