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.

翻译：本文提出了一种利用循环神经网络模拟机械呼吸机压力的方法。传统机械呼吸机的控制压力需由医疗从业者监测，若施加压力不当则可能出现异常。本研究借鉴最新研究成果，开发了一种基于深度序列模型的模拟器，该模型能够根据控制参数与肺部属性的时间序列，预测呼吸吸气阶段呼吸回路中的气道压力。该方法证明了基于神经网络的控制器在追踪压力波形方面显著优于当前行业标准，并为开发高效稳健的压力控制型机械呼吸机提供了重要参考。本文将以每次呼吸吸气阶段预测压力与实际压力之间的平均绝对误差作为评估指标。