Auto-mutual information function of the EEG as a measure of depth of anesthesia
Auto-mutual information function of the EEG as a measure of depth of anesthesia
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脑电图的自动互信息函数作为麻醉深度的测量
DOI：
10.1109/iembs.2011.6090711
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发表时间：
2011
期刊：
影响因子：
--
通讯作者：
P. Caminal
中科院分区：
文献类型：
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作者：
Barbara Julitta;M. Vallverdú;U. Melia;N. Tupaika;M. Jospin;E. Jensen;M. Struys;H. Vereecke;P. Caminal
监测麻醉深度（DOA）是必要的，以减少麻醉中的意识事件，防止延迟恢复阶段。在过去的几十年里，已经提出了一些无创的方法来分析脑电图（EEG），以监测DOA。本研究的目的是将自互信息函数（AMIF）应用于麻醉下患者的脑电图，以寻找能够表征以下4种状态的变量：清醒、镇静、麻醉和爆发抑制发作。结果表明，单个和组合AMIF参数对状态的正确率分别为72.2% ~ 94.1%和61.1% ~ 100%。
Monitoring the depth of anesthesia (DOA) is necessary in order to decrease the incident of awareness in anesthesia and to prevent delays in the recovery phase. In the last decades a number of noninvasive methods have been proposed for the analysis of the electroencephalogram (EEG) for monitoring DOA. The objective of this work was to apply auto mutual information function (AMIF) to EEGs of patients under anesthesia in order to find variables able to characterize the following 4 states: awake, sedated, anesthetized and burst suppression episodes. The results show that the single and combined AMIF parameters were able to correctly classify the states in the range 72.2%–94.1% and 61.1%–100%, respectively.