Narus S P, Kück K, Westenskow D R
Anesthesiology Bioengineering Laboratory, University of Utah, Salt Lake City, USA.
Proc Annu Symp Comput Appl Med Care. 1995:96-100.
A competent breathing circuit is mandatory to the safe and effective delivery of oxygen and anesthetic gases to the patient. Studies have shown that failures in the circuit are the most likely causes of anesthetic mishaps. Unfortunately, the complexity of the system renders traditional monitoring methods ineffective. We have developed a hierarchical artificial neural network monitor that is capable of examining ventilator signals. It was trained to identify 23 faults in the breathing circuit during ventilator controlled breathing and 21 faults during spontaneous breathing. The networks correctly identified a fault condition in 92% and 83% of cases for ventilator and spontaneous data, respectively. The correct fault type was found in 76% and 68% of cases for ventilator and spontaneous data, respectively. Results show that the network met our criteria for a holistic, specific, and vigilant monitoring system.
一个合格的呼吸回路对于向患者安全有效地输送氧气和麻醉气体至关重要。研究表明，回路故障是麻醉事故最可能的原因。不幸的是，该系统的复杂性使得传统监测方法无效。我们开发了一种能够检查呼吸机信号的分层人工神经网络监测器。它经过训练，可在呼吸机控制呼吸期间识别呼吸回路中的23个故障，在自主呼吸期间识别21个故障。该网络在呼吸机数据和自主呼吸数据的病例中，分别有92%和83%正确识别出故障情况。在呼吸机数据和自主呼吸数据的病例中，分别有76%和68%找到了正确的故障类型。结果表明，该网络符合我们对一个全面、具体且警觉的监测系统的标准。
