Mapping Vagus Nerve Stimulation Parameters to Cardiac Physiology using Long Short-term Memory Network
Publication Date
1-1-2021
Document Type
Conference Proceeding
Publication Title
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
DOI
10.1109/EMBC46164.2021.9630667
First Page
5477
Last Page
5480
Abstract
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.
Department
Chemical and Materials Engineering
Recommended Citation
Andrew Branen, Yuyu Yao, Mayuresh V. Kothare, Babak Mahmoudi, and Gautam Kumar. "Mapping Vagus Nerve Stimulation Parameters to Cardiac Physiology using Long Short-term Memory Network" Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (2021): 5477-5480. https://doi.org/10.1109/EMBC46164.2021.9630667
