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Finite state machine implementation for left ventricle modeling and control.

Jacob M King1, Clint A Bergeron1, Charles E Taylor2

  • 1Department of Mechanical Engineering, University of Louisiana at Lafayette, 241 E. Lewis St. RM320, Lafayette, LA, 70503, USA.

Biomedical Engineering Online
|February 1, 2019
PubMed
Summary

A new finite-state machine model simulates left ventricular function, enabling personalized cardiac research and treatment evaluation. This computational approach accurately predicts pressure-volume changes for individual patients.

Keywords:
Cardiovascular lumped parameter modelingLeft ventricular pressure–volume relationshipMock circulatory systemPV loopPatient-specific and population modelingSimulating cardiovascular hemodynamics

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Area of Science:

  • Cardiovascular Physiology
  • Computational Biology
  • Biomedical Engineering

Background:

  • Left ventricle simulation is crucial for evaluating cardiac therapies and operations.
  • Current biomechanical models use continuous states, limiting deterministic processing.
  • A finite-state machine model offers applications in physiological control and high-throughput simulations.

Purpose of the Study:

  • To develop a finite-state machine model for simulating left ventricular pressure-volume control.
  • To create a computational model sensitive to preload, afterload, and contractility.
  • To enable personalized in silico analysis of cardiac function.

Main Methods:

  • A logic-based, conditional finite state machine was designed based on four pressure-volume phases.
  • The model simulates left ventricular function considering preload, afterload, and contractility.
  • Implemented using MathWorks' Simulink and Stateflow tools with a physical hydraulic system.

Main Results:

  • The model accurately simulated changes in preload, afterload, and contractility.
  • Six distinct pressure-volume loop simulations were performed with high fidelity.
  • Achieved errors below 1 mmHg and 1 mL, demonstrating acceptable performance for deterministic systems.

Conclusions:

  • The computational model enables personalized, in silico simulation of cardiac function using individual patient data.
  • The architecture supports execution on deterministic systems for experimental validation.
  • Provides a mock circulatory system for investigating individual pathophysiology and predictive analysis.