This paper presents a recurrent neural network approach to simulating mechanical ventilator pressure. The traditional mechanical ventilator has a control pressure that is monitored by a medical practitioner and can behave incorrectly if the proper pressure is not applied. This paper takes advantage of recent research and develops a simulator based on a deep sequence model to predict airway pressure in the respiratory circuit during the inspiratory phase of a breath given a time series of control parameters and lung attributes. This method demonstrates the effectiveness of neural network-based controllers in tracking pressure wave forms significantly better than the current industry standard and provides insights into the development of effective and robust pressure-controlled mechanical ventilators. The paper will measure as the mean absolute error between the predicted and actual pressures during the inspiratory phase of each breath.
Cite
@article{arxiv.2410.06552,
title = {Ventilator pressure prediction using recurrent neural network},
author = {Su Diao and Changsong Wei and Junyu Wang and Yizhou Li},
journal= {arXiv preprint arXiv:2410.06552},
year = {2024}
}