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Purpose Left ventricular assist devices (LVADs) play a vital role for end-stage heart failure (HF) patients during destination therapy. However, the speed of LVADs are set at a constant speed mode which can result in insufficient perfusion and hazardous events such as ventricular suction and pulmonary congestion. A preload-based physiological control system, a “smart pump”, can be employed to adjust the LVAD speed automatically and therefore improve prefusion and deal with the hazardous events. Although most of these physiological control systems require direct measurement of preload, long-term implantation of a pressure sensor can lead to drift or thrombus formation. Preload can instead be estimated using pump intrinsic variables. This study presents a sensorless control of LVAD across different patient conditions without relying on any implantable sensors. Methods An adaptive preload-based physiological controller was employed to adjust the speed of HeartWare HVAD pump to handle interpatient and intrapatient variations using a computer simulation. A deep convolutional neural network (CNN) model was designed to estimated preload based on the 600 samples of pump flow in real-time mode. The proposed CNN model was trained and validated on 100 different patient conditions and tested over new 30 patient conditions and a range of changes to the cardiovascular system, including changes in PVR, SVR and rest to exercise. Finally, the physiological controller and preload estimator were combined to create a sesnorless preload-based physiological control system for LVADs. Results Preload was accurately estimated using the proposed deep convolutional neural network model, which was successfully trained, tested and validated using computer simulation data. The results for all scenarios show the correlation coefficient of 0.97 and RMSE of 0.84 (mmHg). Furthermore, the sesnorless preload-based physiological control was able to prevent ventricular suction and pulmonary congestions over new 30 patients. Conclusion The sesnorless preload-based physiological control can be useful for estimation of preload and control of LVAD speed in a real-time mode for HF patients without implantation of pressure sensors. This controller can improve the HF patients’ quality of lives and lifespan by increasing perfusion, preventing suction and congestion. |
