In vitro evaluation of multi-objective physiological control of the centrifugal blood pump.
Tarcisio Fernandes LeaoBruno Utiyama da SilvaJeison Willian Gomes da FonsecaEduardo Guy Perpetuo BockAron AndradePublished in: Artificial organs (2020)
Left ventricular assist devices (LVADs) have been used as a bridge to transplantation or as destination therapy to treat patients with heart failure (HF). The inability of control strategy to respond automatically to changes in hemodynamic conditions can impact the patients' quality of life. The developed control system/algorithm consists of a control system that harmoniously adjusts pump speed without additional sensors, considering the patient's clinical condition and his physical activity. The control system consists of three layers: (a) Actuator speed control; (b) LVAD flow control (FwC); and (c) Fuzzy control system (FzC), with the input variables: heart rate (HR), mean arterial pressure (MAP), minimum pump flow, level of physical activity (data from patient), and clinical condition (data from physician, INTERMACS profile). FzC output is the set point for the second LVAD control schemer (FwC) which in turn adjusts the speed. Pump flow, MAP, and HR are estimated from actuator drive parameters (speed and power). Evaluation of control was performed using a centrifugal blood pump in a hybrid cardiovascular simulator, where the left heart function is the mechanical model and right heart function is the computational model. The control system was able to maintain MAP and cardiac output in the physiological level, even under variation of EF. Apart from this, also the rotational pump speed is adjusted following the simulated clinical condition. No backflow from the aorta in the ventricle occurred through LVAD during tests. The control algorithm results were considered satisfactory for simulations, but it still should be confirmed during in vivo tests.
Keyphrases
- physical activity
- left ventricular
- heart rate
- emergency department
- body mass index
- blood pressure
- machine learning
- bone marrow
- stem cells
- coronary artery disease
- coronary artery
- depressive symptoms
- mesenchymal stem cells
- heart rate variability
- chronic kidney disease
- prognostic factors
- sensitive detection
- quantum dots
- pulmonary hypertension
- left ventricular assist device
- single molecule
- molecular dynamics
- transcatheter aortic valve replacement
- acute coronary syndrome
- electronic health record
- hypertrophic cardiomyopathy
- big data
- high density