Finite state machine implementation for left ventricle modeling and control
Abstract Background Simulation of a left ventricle has become a critical facet of evaluating therapies and operations that interact with cardiac performance. The ability to simulate a wide range of possible conditions, changes in cardiac performance, and production of nuisances at transition points...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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BMC
2019-01-01
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| Series: | BioMedical Engineering OnLine |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12938-019-0628-3 |
| _version_ | 1819088524773687296 |
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| author | Jacob M. King Clint A. Bergeron Charles E. Taylor |
| author_facet | Jacob M. King Clint A. Bergeron Charles E. Taylor |
| author_sort | Jacob M. King |
| collection | DOAJ |
| description | Abstract Background Simulation of a left ventricle has become a critical facet of evaluating therapies and operations that interact with cardiac performance. The ability to simulate a wide range of possible conditions, changes in cardiac performance, and production of nuisances at transition points enables evaluation of precision medicine concepts that are designed to function through this spectrum. Ventricle models have historically been based on biomechanical analysis, with model architectures constituted of continuous states and not conducive to deterministic processing. Producing a finite-state machine governance of a left ventricle model would enable a broad range of applications: physiological controller development, experimental left ventricle control, and high throughput simulations of left ventricle function. Methods A method for simulating left ventricular pressure-volume control utilizing a preload, afterload, and contractility sensitive computational model is shown. This approach uses a logic-based conditional finite state machine based on the four pressure-volume phases that describe left ventricular function. This was executed with a physical system hydraulic model using MathWorks’ Simulink® and Stateflow tools. Results The approach developed is capable of simulating changes in preload, afterload, and contractility in time based on a patient’s preload analysis. Six pressure–volume loop simulations are presented to include a base-line, preload change only, afterload change only, contractility change only, a clinical control, and heart failure with normal ejection fraction. All simulations produced an error of less than 1 mmHg and 1 mL of the absolute difference between the desired and simulated pressure and volume set points. The acceptable performance of the fixed-timestep architecture in the finite state machine allows for deployment to deterministic systems, such as experimental systems for validation. Conclusions The proposed approach allows for personalized data, revealed through an individualized clinical pressure–volume analysis, to be simulated in silico. The computational model architecture enables this control structure to be executed on deterministic systems that govern experimental left ventricles. This provides a mock circulatory system with the ability to investigate the pathophysiology for a specific individual by replicating the exact pressure–volume relationship defined by their left ventricular functionality; as well as perform predictive analysis regarding changes in preload, afterload, and contractility in time. |
| first_indexed | 2024-12-21T21:53:25Z |
| format | Article |
| id | doaj.art-9aff4cda48b24af9bbcc93d73450e92a |
| institution | Directory Open Access Journal |
| issn | 1475-925X |
| language | English |
| last_indexed | 2024-12-21T21:53:25Z |
| publishDate | 2019-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BioMedical Engineering OnLine |
| spelling | doaj.art-9aff4cda48b24af9bbcc93d73450e92a2022-12-21T18:49:02ZengBMCBioMedical Engineering OnLine1475-925X2019-01-0118112410.1186/s12938-019-0628-3Finite state machine implementation for left ventricle modeling and controlJacob M. King0Clint A. Bergeron1Charles E. Taylor2Department of Mechanical Engineering, University of Louisiana at LafayetteDepartment of Mechanical Engineering, University of Louisiana at LafayetteDepartment of Mechanical Engineering, University of Louisiana at LafayetteAbstract Background Simulation of a left ventricle has become a critical facet of evaluating therapies and operations that interact with cardiac performance. The ability to simulate a wide range of possible conditions, changes in cardiac performance, and production of nuisances at transition points enables evaluation of precision medicine concepts that are designed to function through this spectrum. Ventricle models have historically been based on biomechanical analysis, with model architectures constituted of continuous states and not conducive to deterministic processing. Producing a finite-state machine governance of a left ventricle model would enable a broad range of applications: physiological controller development, experimental left ventricle control, and high throughput simulations of left ventricle function. Methods A method for simulating left ventricular pressure-volume control utilizing a preload, afterload, and contractility sensitive computational model is shown. This approach uses a logic-based conditional finite state machine based on the four pressure-volume phases that describe left ventricular function. This was executed with a physical system hydraulic model using MathWorks’ Simulink® and Stateflow tools. Results The approach developed is capable of simulating changes in preload, afterload, and contractility in time based on a patient’s preload analysis. Six pressure–volume loop simulations are presented to include a base-line, preload change only, afterload change only, contractility change only, a clinical control, and heart failure with normal ejection fraction. All simulations produced an error of less than 1 mmHg and 1 mL of the absolute difference between the desired and simulated pressure and volume set points. The acceptable performance of the fixed-timestep architecture in the finite state machine allows for deployment to deterministic systems, such as experimental systems for validation. Conclusions The proposed approach allows for personalized data, revealed through an individualized clinical pressure–volume analysis, to be simulated in silico. The computational model architecture enables this control structure to be executed on deterministic systems that govern experimental left ventricles. This provides a mock circulatory system with the ability to investigate the pathophysiology for a specific individual by replicating the exact pressure–volume relationship defined by their left ventricular functionality; as well as perform predictive analysis regarding changes in preload, afterload, and contractility in time.http://link.springer.com/article/10.1186/s12938-019-0628-3Left ventricular pressure–volume relationshipPV loopMock circulatory systemCardiovascular lumped parameter modelingSimulating cardiovascular hemodynamicsPatient-specific and population modeling |
| spellingShingle | Jacob M. King Clint A. Bergeron Charles E. Taylor Finite state machine implementation for left ventricle modeling and control BioMedical Engineering OnLine Left ventricular pressure–volume relationship PV loop Mock circulatory system Cardiovascular lumped parameter modeling Simulating cardiovascular hemodynamics Patient-specific and population modeling |
| title | Finite state machine implementation for left ventricle modeling and control |
| title_full | Finite state machine implementation for left ventricle modeling and control |
| title_fullStr | Finite state machine implementation for left ventricle modeling and control |
| title_full_unstemmed | Finite state machine implementation for left ventricle modeling and control |
| title_short | Finite state machine implementation for left ventricle modeling and control |
| title_sort | finite state machine implementation for left ventricle modeling and control |
| topic | Left ventricular pressure–volume relationship PV loop Mock circulatory system Cardiovascular lumped parameter modeling Simulating cardiovascular hemodynamics Patient-specific and population modeling |
| url | http://link.springer.com/article/10.1186/s12938-019-0628-3 |
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