A Sensorless Control System for an Implantable Heart Pump using a
Real-time Deep Convolutional Neural Network
Abstract
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.