Monitoring the Depth of Anesthesia Through the Use of Cerebral Hemodynamic Measurements Based on Sample Entropy Algorithm
Monitoring the Depth of Anesthesia Through the Use of Cerebral Hemodynamic Measurements Based on Sample Entropy Algorithm
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基于样本熵算法的脑血流动力学测量监测麻醉深度
DOI：
10.1109/tbme.2019.2921362
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发表时间：
2020-03
影响因子：
4.6
通讯作者：
Xiangguo Yan
中科院分区：
文献类型：
--
作者：
Gang Wang;Zhian Liu;Yiming Feng;jinming Li;Hailong Dong;Dong Wang;Jing Li;Nong Yan;Tun Liu;Xiangguo Yan
Objective: The aim of this study is to explore the relationship between the depth of anesthesia and the cerebral hemodynamic variables during the complete anesthesia process. Methods: In this study, near-infrared spectroscopy signals were used to record eight kinds of cerebral hemodynamic variables, including left, right, proximal, distal deoxygenated (Hb) and oxygenated (HbO2) hemoglobin concentration changes. Then, by measuring the complexity information of cerebral hemodynamic variables, the sample entropy was calculated as a new index of monitoring the depth of anesthesia. Results: By means of receiver operating characteristic curve analysis, the sample entropy approach was proved to effectively discriminate anesthesia maintenance and waking phases. The discriminatory ability of HbO2 signals was stronger than that of Hb signals and the distal signals had weaker discrimination capability when compared with the proximal signals. In addition, there was statistical consistency between the bispectral index and sample entropy of cerebral hemodynamic variables during the complete anesthesia process. Moreover, the cerebral hemodynamic signals could not be interfered by clinical electrical devices. Conclusion: The sample entropy of cerebral hemodynamic variables could be suitable as a new index for monitoring the depth of anesthesia. Significance: This study is very meaningful for developing new modality and decoding methods in perspective of anesthesia surveillance and may result in the anesthesia monitoring system with high performance.
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DOI：
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发表时间：
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期刊：
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