In vitro evaluation of multi-objective physiological control of the centrifugal blood pump

A Multi-objective Physiological Control for Continuous Flow Left Ventricular Assist Devices: Comparison of Estimator versus Sensor-based Feedback. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023;2023:1-5. [PMID: 38082905 DOI: 10.1109/embc40787.2023.10340974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Left Ventricular Assist Devices have been successfully used for the treatment of Congestive Heart failure in patients who are not eligible for heart transplantation. This paper describes the implementation and comparison of the performance of a pressure sensor-based feedback controller. The strategies were tested on a mock loop of the systemic circulation. The results show that the use of pressure sensors generated a more accurate response of the controller compared to the use of estimators.Clinical Relevance- The study describes the integration of a LVAD physiological controller to a dynamic wireless monitoring system.

Control Strategy Design of a Microblood Pump Based on Heart-Rate Feedback

Based on the nonlinear relationship between heart rate and stroke volume, a flow model of left ventricular circulation was improved, and a variable-speed blood-pump control strategy based on heart-rate feedback was proposed. The control strategy was implemented on a system combining the rotary blood pump and blood circulation models of heart failure. The aortic flow of a healthy heart at different heart rates was the desired control goal. Changes in heart rate were monitored and pump speed was adjusted so that the output flow and aortic pressure of the system would match a normal heart in real time to achieve the best auxiliary state. After simulation with MATLAB, the cardiac output satisfied the ideal perfusion requirements at different heart rates, and aortic pressure demonstrated lifting and had good pulsatile performance when a variable-speed blood pump was used. The coupled model reflected the relationship between hemodynamic parameters at different heart rates with the use of the variable-speed blood pump, providing a theoretical basis for the blood-pump-assisted treatment of heart failure and the design of physiological control strategies.

A Sensorless Control System for an Implantable Heart Pump Using a Real-Time Deep Convolutional Neural Network

Left ventricular assist devices (LVADs) are mechanical pumps, which can be used to support heart failure (HF) patients as bridge to transplant and destination therapy. To automatically adjust the LVAD speed, a physiological control system needs to be designed to respond to variations of patient hemodynamics across a variety of clinical scenarios. These control systems require pressure feedback signals from the cardiovascular system. However, there are no suitable long-term implantable sensors available. In this study, a novel real-time deep convolutional neural network (CNN) for estimation of preload based on the LVAD flow was proposed. A new sensorless adaptive physiological control system for an LVAD pump was developed using the full dynamic form of model free adaptive control (FFDL-MFAC) and the proposed preload estimator to maintain the patient conditions in safe physiological ranges. The CNN model for preload estimation was trained and evaluated through 10-fold cross validation on 100 different patient conditions and the proposed sensorless control system was assessed on a new testing set of 30 different patient conditions across six different patient scenarios. The proposed preload estimator was extremely accurate with a correlation coefficient of 0.97, root mean squared error of 0.84 mmHg, reproducibility coefficient of 1.56 mmHg, coefficient of variation of 14.44%, and bias of 0.29 mmHg for the testing dataset. The results also indicate that the proposed sensorless physiological controller works similarly to the preload-based physiological control system for LVAD using measured preload to prevent ventricular suction and pulmonary congestion. This study shows that the LVADs can respond appropriately to changing patient states and physiological demands without the need for additional pressure or flow measurements.