Orr J A, Westenskow D R
Department of Anesthesiology, University of Utah, Salt Lake City 84132.
J Clin Monit. 1994 Mar;10(2):101-9. doi: 10.1007/BF02886822.
The objectives of our study were (1) to implement intelligent respiratory alarms with a neural network; and (2) to increase alarm specificity and decrease false-alarm rates compared with current alarms.
We trained a neural network to recognize 13 faults in an anesthesia breathing circuit. The system extracted 30 breath-to-breath features from the airway CO2, flow, and pressure signals. We created training data for the network by introducing 13 faults repeatedly in 5 dogs (616 total faults). We used the data to train the neural network using the backward error propagation algorithm.
In animals, the trained network reported the alarms correctly for 95.0% of the faults when tested during controlled ventilation, and for 86.9% of the faults during spontaneous breathing. When tested in the operating room, the system found and correctly reported 54 of 57 faults that occurred during 43.6 hr of use. The alarm system produced a total of 74 false alarms during 43.6 hr of monitoring.
Neural networks may be useful in creating intelligent anesthesia alarm systems.
我们研究的目的是（1）利用神经网络实现智能呼吸警报；（2）与当前警报相比，提高警报特异性并降低误报率。
我们训练了一个神经网络来识别麻醉呼吸回路中的13种故障。该系统从气道二氧化碳、流量和压力信号中提取30个逐次呼吸特征。我们通过在5只狗身上反复引入13种故障（共616个故障）来为网络创建训练数据。我们使用这些数据通过反向误差传播算法训练神经网络。
在动物中，经过训练的网络在控制通气期间测试时，对95.0%的故障正确报告了警报，在自主呼吸期间对86.9%的故障正确报告了警报。在手术室进行测试时，该系统在43.6小时的使用过程中发现并正确报告了57个故障中的54个。警报系统在43.6小时的监测期间总共产生了74次误报。
神经网络可能有助于创建智能麻醉警报系统。
