BACKGROUND AND OBJECTIVE Mismatch between invasive mechanical ventilation and the requirements of patients results in patient-ventilator asynchrony (PVA), which is associated with a series of adverse clinical outcomes. Although the efficiency of the available approaches for automatically detecting various types of PVA from the ventilator waveforms is unsatisfactory, the feasibility of powerful deep learning approaches in addressing this problem has not been investigated. METHODS We propose a 2-layer long short-term memory (LSTM) network to detect two most frequently encountered types of PVA, namely, double triggering (DT) and ineffective inspiratory effort during expiration (IEE), on two datasets. The performance of the networks is evaluated first using cross-validation on the combined dataset, and then using a cross testing scheme, in which the LSTM networks are established on one dataset and tested on the other. RESULTS Compared with the reported rule-based algorithms and the machine learning models, the proposed 2-layer LSTM network exhibits the best overall performance, with the F1 scores of 0.983 and 0.979 for DT and IEE detection, respectively, on the combined dataset. Furthermore, it outperforms the other approaches in cross testing. CONCLUSIONS The findings suggest that LSTM is an excellent technique for accurate recognition of PVA in clinics. Such a technique can help detect and correct PVA for a better patient ventilator interaction.

中文翻译：

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使用两层长短期记忆神经网络从机械通气波形中检测患者-呼吸机的异步性。

背景与目的侵入性机械通气与患者需求之间的不匹配会导致患者呼吸机异步（PVA），这与一系列不良的临床结果相关。尽管从呼吸机波形自动检测各种类型的PVA的可用方法的效率不尽人意，但尚未研究强大的深度学习方法解决此问题的可行性。方法我们提出了一个2层长短期记忆（LSTM）网络，以在两个数据集上检测两种最常见的PVA类型，即两次触发（DT）和呼气过程中无效的吸气量（IEE）。首先对合并后的数据集进行交叉验证，然后再使用交叉测试方案评估网络的性能，LSTM网络建立在一个数据集上，并在另一个数据集上进行测试。结果与所报告的基于规则的算法和机器学习模型相比，所提出的2层LSTM网络表现出最佳的整体性能，在组合数据集上，DT和IEE检测的F1分数分别为0.983和0.979。此外，它在交叉测试中优于其他方法。结论结论表明，LSTM是一种在临床中准确识别PVA的出色技术。这种技术可以帮助检测和纠正PVA，以更好地与患者呼吸机互动。组合数据集上DT和IEE检测的F1分数分别为0.983和0.979。此外，它在交叉测试中优于其他方法。结论结论表明，LSTM是一种在临床中准确识别PVA的出色技术。这种技术可以帮助检测和纠正PVA，以更好地与患者呼吸机互动。组合数据集上DT和IEE检测的F1分数分别为0.983和0.979。此外，它在交叉测试中优于其他方法。结论结论表明，LSTM是一种在临床中准确识别PVA的出色技术。这种技术可以帮助检测和纠正PVA，以更好地与患者呼吸机互动。