Sensorless cardiac phase detection for synchronized control of ventricular assist devices using nonlinear kernel regression model

Recently, driving methods for synchronizing ventricular assist devices (VADs) with heart rhythm of patients suffering from severe heart failure have been receiving attention. Most of the conventional methods require implanting a sensor for measurement of a signal, such as electrocardiogram, to achieve synchronization. In general, implanting sensors into the cardiovascular system of the patients is undesirable in clinical situations. The objective of this study was to extract the heartbeat component without any additional sensors, and to synchronize the rotational speed of the VAD with this component. Although signals from the VAD such as the consumption current and the rotational speed are affected by heartbeat, these raw signals cannot be utilized directly in the heartbeat synchronization control methods because they are changed by not only the effect of heartbeat but also the change in the rotational speed itself. In this study, a nonlinear kernel regression model was adopted to estimate the instantaneous rotational speed from the raw signals. The heartbeat component was extracted by computing the estimation error of the model with parameters determined by using the signals when there was no effect of heartbeat. Validations were conducted on a mock circulatory system, and the heartbeat component was extracted well by the proposed method. Also, heartbeat synchronization control was achieved without any additional sensors in the test environment.