Petersen Christian Leth, Görges Matthias, Massey Roslyn, Dumont Guy Albert, Ansermino J Mark
From the *Department of Anesthesiology, Pharmacology & Therapeutics, University of British Columbia, Vancouver, British Columbia, Canada; †Pediatric Anesthesia Research Team, Child and Family Research Institute, Vancouver, British Columbia, Canada; and ‡Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, British Columbia, Canada.
Anesth Analg. 2016 Nov;123(5):1136-1140. doi: 10.1213/ANE.0000000000001506.
Recent research and advances in the automation of anesthesia are driving the need to better understand electroencephalogram (EEG)-based anesthesia end points and to test the performance of anesthesia monitors. This effort is currently limited by the need to collect raw EEG data directly from patients.
A procedural method to synthesize EEG signals was implemented in a mobile software application. The application is capable of sending the simulated signal to an anesthesia depth of hypnosis monitor. Systematic sweeps of the simulator generate functional monitor response profiles reminiscent of how network analyzers are used to test electronic components.
Three commercial anesthesia monitors (Entropy, NeuroSENSE, and BIS) were compared with this new technology, and significant response and feature variations between the monitor models were observed; this includes reproducible, nonmonotonic apparent multistate behavior and significant hysteresis at light levels of anesthesia.
Anesthesia monitor response to a procedural simulator can reveal significant differences in internal signal processing algorithms. The ability to synthesize EEG signals at different anesthetic depths potentially provides a new method for systematically testing EEG-based monitors and automated anesthesia systems with all sensor hardware fully operational before human trials.
麻醉自动化方面的最新研究与进展促使人们更深入地了解基于脑电图（EEG）的麻醉终点，并对麻醉监测仪的性能进行测试。目前，这一工作因需要直接从患者身上收集原始EEG数据而受到限制。
在一款移动软件应用程序中实现了一种合成EEG信号的程序方法。该应用程序能够将模拟信号发送至麻醉深度催眠监测仪。模拟器的系统扫描生成了功能性监测仪响应曲线，类似于网络分析仪用于测试电子元件的方式。
将三种商用麻醉监测仪（熵指数监测仪、神经感觉监测仪和脑电双频指数监测仪）与这项新技术进行了比较，观察到各监测仪型号之间存在显著的响应和特征差异；这包括可重复的、非单调的明显多状态行为以及在浅麻醉水平下的显著滞后现象。
麻醉监测仪对程序模拟器的响应能够揭示内部信号处理算法的显著差异。在不同麻醉深度合成EEG信号的能力可能为在人体试验之前，在所有传感器硬件全功能运行的情况下，系统测试基于EEG的监测仪和自动化麻醉系统提供一种新方法。
