Non-invasive estimation and control of inlet pressure in an implantable rotary blood pump for heart failure patients

We propose a dynamical model for mean inlet pressure estimation in an implantable rotary blood pump during the diastolic period. Non-invasive measurements of pump impeller rotational speed (omega), motor power (P), and pulse width modulation signal acquired from the pump controller were used as inputs to the model. The model was validated over a wide range of speed ramp studies, including (i) healthy (C1), variations in (ii) heart contractility (C2); (iii) afterload (C2, C3, C4), and (iv) preload (C5, C6, C7). Linear regression analysis between estimated and extracted mean inlet pressure obtained from in vivo animal data (greyhound dogs, N = 3) resulted in a highly significant correlation coefficients (R(2) = 0.957, 0.961, 0.958, 0.963, 0.940, 0.946, and 0.959) and mean absolute errors of (e = 1.604, 2.688, 3.667, 3.990, 2.791, 3.215, and 3.225 mmHg) during C1, C2, C3, C4, C5, C6, and C7, respectively. The proposed model was also used to design a controller to regulate mean diastolic pump inlet pressure using non-invasively measured omega and P. In the presence of model uncertainty, the controller was able to track and settle to the desired input within a finite number of sampling periods and minimal error (0.92 mmHg). The model developed herein will play a crucial role in developing a robust control system of the pump that detects and thus avoids undesired pumping states by regulating the inlet pressure within a predefined physiologically realistic limit