Mapping Vagus Nerve Stimulation Parameters to Cardiac Physiology using Long Short-term Memory Network.

Vagus nerve stimulation (VNS) is an emerging therapeutic strategy for pathological conditions in a variety of diseases; however, several challenges arise for applying this stimulation paradigm in automated closed-loop control. In this work, we propose a data driven approach for predicting the impact of VNS on physiological variables. We apply this approach on a synthetic dataset created with a physiological model of a rat heart. Through training several neural network models, we found that a long short term memory (LSTM) architecture gave the best performance on a test set. Further, we found the neural network model was capable of mapping a set of VNS parameters to the correct response in the heart rate and the mean arterial blood pressure. In closed-loop control of biological systems, a model of the physiological system is often required and we demonstrate using a data driven approach to meet this requirement in the cardiac system.