Bakkes T H G F, Montree R J H, Mischi M, Mojoli F, Turco S
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:150-153. doi: 10.1109/EMBC44109.2020.9175796.
Patients suffering from respiratory failure are often put on assisted mechanical ventilation. Patient-ventilator asynchrony (PVA) can occur during mechanical ventilation, which cause damage to the lungs and has been linked to increased mortality in the intensive care unit. In current clinical practice PVA is still detected using visual inspection of the air pressure, flow, and volume curves, which is time-consuming and sensitive to subjective interpretation. Correct detection of the patient respiratory efforts is needed to properly asses the type of asynchrony. Therefore, we propose a method for automatic detection of the patient respiratory efforts using a one-dimensional convolution neural network. The proposed method was able to detect patient efforts with a sensitivity and precision of 98.6% and 97.3% for the inspiratory efforts, and 97.7% and 97.2% for the expiratory efforts. Besides allowing detection of PVA, combining the estimated timestamps of patient's inspiratory and expiratory efforts with the timings of the mechanical ventilator further allows for classification of the asynchrony type. In the future, the proposed method could support clinical decision making by informing clinicians on the quality of ventilation and providing actionable feedback for properly adjusting the ventilator settings.
患有呼吸衰竭的患者常常需要接受机械辅助通气。在机械通气过程中可能会出现患者 - 呼吸机不同步（PVA）的情况，这会对肺部造成损害，并且与重症监护病房死亡率的增加有关。在当前临床实践中，仍通过目视检查气压、流量和容积曲线来检测PVA，这既耗时又容易受到主观解读的影响。需要正确检测患者的呼吸努力，以便准确评估不同步的类型。因此，我们提出了一种使用一维卷积神经网络自动检测患者呼吸努力的方法。所提出的方法能够检测患者的呼吸努力，吸气努力的灵敏度和精度分别为98.6%和97.3%，呼气努力的灵敏度和精度分别为97.7%和97.2%。除了能够检测PVA外，将患者吸气和呼气努力的估计时间戳与机械呼吸机的时间相结合，还可以对不同步类型进行分类。未来，所提出的方法可以通过向临床医生告知通气质量并提供可操作的反馈以正确调整呼吸机设置，来支持临床决策。
