Kortelainen Jukka, Väyrynen Eero, Jia Xiaofeng, Seppänen Tapio, Thakor Nitish
Department of Computer Science and Engineering, BOX 4500, FIN-90014 University of Oulu, Finland.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:4279-82. doi: 10.1109/EMBC.2012.6346912.
In animal studies, reliable measures for depth of anesthesia are frequently required. Previous findings suggest that the continuous depth of anesthesia indices developed for humans might not be adequate for rats whose EEG changes during anesthesia represent more of quick transitions between discrete states. In this paper, the automatic EEG-based detection of awakening from anesthesia was studied in rats. An algorithm based on Bayesian Information Criterion (BIC) is proposed for the assessment of the switch-like change in the signal characteristics occurring just before the awakening. The method was tested with EEGs recorded from ten rats recovering from isoflurane anesthesia. The algorithm was shown to be able to detect the sudden change in the EEG related to the moment of awakening with a precision comparable to careful visual inspection. Our findings suggest that monitoring such signal changes may offer an interesting alternative to the application of continuous depth of anesthesia indices when avoiding the awakening of the animal during e.g. a clinical experiment.
在动物研究中，经常需要可靠的麻醉深度测量方法。先前的研究结果表明，为人类开发的连续麻醉深度指标可能不适用于大鼠，因为大鼠在麻醉期间的脑电图变化更多地表现为离散状态之间的快速转变。本文研究了基于脑电图自动检测大鼠麻醉苏醒的方法。提出了一种基于贝叶斯信息准则（BIC）的算法，用于评估苏醒前信号特征的类似开关的变化。该方法用从十只从异氟烷麻醉中恢复的大鼠记录的脑电图进行了测试。结果表明，该算法能够检测出与苏醒时刻相关的脑电图突然变化，其精度与仔细的目视检查相当。我们的研究结果表明，在例如临床实验中避免动物苏醒时，监测此类信号变化可能为应用连续麻醉深度指标提供一个有趣的替代方法。
