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Estimation of Simulated Left Ventricle Elastance Using Lumped Parameter Modelling and Gradient-Based Optimization

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Area of Science:

  • Cardiovascular physiology
  • Computational modeling
  • Biomedical engineering

Background:

  • Accurate estimation of cardiac parameters is crucial for diagnosing and managing cardiovascular diseases.
  • Lumped parameter models offer a simplified yet effective approach to simulating cardiovascular dynamics.
  • Noninvasive measurements are preferred for clinical assessments but require robust algorithms for parameter extraction.

Purpose of the Study:

  • To evaluate a parameter discovery approach using a lumped parameter cardiovascular model and optimization.
  • To approximate key cardiac parameters, including simulated left ventricle elastances.
  • To assess the accuracy of estimated parameters using synthetic data from healthy and diseased heart models.

Main Methods:

  • Utilized a lumped parameter model of the cardiovascular system coupled with gradient optimization.
  • Employed forward-mode automatic differentiation for cost function-parameter matrix estimation, comparing it with finite differences.
  • Generated synthetic data mimicking noninvasive clinical measurements for healthy and diseased heart conditions.

Main Results:

  • A hybrid optimization strategy outperformed first-order optimization and finite difference methods.
  • Mean absolute percentage errors for estimated parameters ranged from 6.67% to 14.14%.
  • Left ventricle elastance estimation errors were minimal (~2%) for simulated aortic stenosis and mitral regurgitation using arterial pressure and valvular flow rate data.

Conclusions:

  • The developed parameter discovery approach effectively estimates critical cardiac parameters, including left ventricle elastances.
  • Hybrid optimization strategies combined with automatic differentiation provide accurate estimations for cardiovascular models.
  • The inclusion of volume trends alongside pressure and flow data can improve left ventricle pressure waveform tracking, despite slightly increased elastance estimation errors.