Evaluation of Fluid Resuscitation Control Algorithms via a Hardware-in-the-Loop Test Bed

• Published in: IEEE transactions on biomedical engineering 2020-02, Vol.67 (2), p.471-481
• Main Authors: Mirinejad, Hossein, Parvinian, Bahram, Ricks, Margo, Zhang, Yi, Weininger, Sandy, Hahn, Jin-Oh, Scully, Christopher G.
• Format: Article
• Language: English
• Subjects: Algorithms; Biomedical monitoring; Blood Pressure - physiology; Blood Pressure Determination; Computational modeling; Computer applications; Computer Simulation; Control algorithms; Control systems; Controllers; Cyber-physical system; Disturbances; fluid resuscitation control; Fluid Therapy - methods; Fluids; Hardware-in-the-loop simulation; hardware-in-the-loop testing; Hemodynamic responses; Hemodynamics; Hemodynamics - physiology; Humans; In vivo methods and tests; Iterative methods; Mathematical models; Monitoring; Performance evaluation; physiologic closed-loop control systems; Proportional integral derivative; Resuscitation; Resuscitation - methods; Software; Testing
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.2019.2915526

Objective: This paper presents a hardware-in-the-loop (HIL) testing platform for evaluating the performance of fluid resuscitation control algorithms. The proposed platform is a cyber-physical system that integrates physical devices with computational models and computer-based algorithms. Methods: The HIL test bed is evaluated against in silico and in vivo data to ensure the hemodynamic variables are appropriately predicted in the proposed platform. The test bed is then used to investigate the performance of two fluid resuscitation control algorithms: a decision table (rule-based) and a proportional-integral-derivative (PID) controller. Results: The statistical evaluation of test bed indicates that similar results are observed in the HIL test bed, in silico implementation, and the in vivo data, verifying that the HIL test bed can adequately predict the hemodynamic responses. Comparison of the two fluid resuscitation controllers reveals that both controllers stabilized hemodynamic variables over time and had similar speed to efficiently achieve the target level of the hemodynamic endpoint. However, the accuracy of the PID controller was higher than the rule-based for the scenarios tested in the HIL platform. Conclusion: The results demonstrate the potential of the HIL test bed for realistic testing of physiologic controllers by incorporating physical devices with computational models of physiology and disturbances. Significance: This type of testing enables relatively fast evaluation of physiologic closed-loop control systems to aid in iterative design processes and offers complementary means to existing techniques (e.g., in silico, in vivo, and clinical studies) for testing of such systems against a wide range of disturbances and scenarios.