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Convolutional Neural Network for Respiratory Mechanics Estimation during Pressure Support Ventilation

机译：压力支持通风过程中呼吸力学估算的卷积神经网络

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In mechanically ventilated patients, some lung injuries can be reduced or avoided with therapy individualization, while the lung function is evaluated continuously, breath by breath. However, obtaining information on respiratory mechanics (respiratory system resistance and compliance) in the presence of respiratory effort is challenging, even if using invasive and complex procedures. The contribution of this work is to predict both respiratory system resistance and compliance over time using a convolutional neural network (CNN) and estimate the respiratory effort profile using the respiratory dynamics. Therefore, the approach used in this work was to generate a large amount of simulated data to feed a CNN so it could learn how to predict the correct values of the respiratory system resistance and compliance. Then, the respiratory effort was estimated by solving a first-order linear model. The main results showed a normalized mean squared error of 5.7% for the respiratory system resistance and 11.56% for compliance from Bland-Altman plots derived from the computational simulator. Finally, the method was validated using real data from an active lung simulator within which respiratory mechanics varied, and some ventilator settings were adjusted to mimic actual patient situations. The active lung simulator effort profile was obtained with a normalized mean squared error of 8.31% considering the use of an active lung simulator. The results have shown that the simulated data were valuable for the CNN training, while the performance over the real data suggested that the network was generalized accordingly for estimating respiratory parameters and effort profile.
机译：在机械通风患者中，可以减少或避免治疗个体化的一些肺功量，而肺功能连续评估，呼吸呼吸。然而，在呼吸努力存在下获得有关呼吸力学（呼吸系统抵抗和合规性）的信息是具有挑战性的，即使使用侵入性和复杂的程序也是如此。这项工作的贡献是使用卷积神经网络（CNN）预测呼吸系统阻力和遵守时间，并使用呼吸动力学估计呼吸努力概况。因此，本作工作中使用的方法是生成大量模拟数据来馈送CNN，因此可以学习如何预测呼吸系统阻力和符合性的正确值。然后，通过求解一阶线性模型来估计呼吸努力。主要结果表明，归一化平均平均误差为5.7％，呼吸系统的抵抗力为11.56％，符合来自计算模拟器的Bland-Altman图。最后，使用来自活性肺模拟器的真实数据验证该方法，其中呼吸力学变化，并调整了一些呼吸机设置以模拟实际患者情况。考虑到有源肺模拟器的归一化平均平方误差，获得了有源肺模拟器努力概况。结果表明，模拟数据对于CNN训练来说是有价值的，而实际数据的性能表明该网络被广泛地估计了呼吸参数和努力轮廓。

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