麻醉深度监测对提高麻醉质量, 保障患者的手术期安全与康复具有极为重要的意义。系统地回顾了脑电信号分析算法, 主要包括脑电双频指数、麻醉趋势指数、脑状态指数等多种不同参数的算法在麻醉深度监测中的应用研究及进展情况。首先比较了这些参数的脑电分析算法在临床应用中的优缺点, 接着提出了具有优秀去噪能力的排序熵指数算法, 并引入反向映射神经网络对脑电双频指数、麻醉趋势和排序熵指数进行校正优化, 以便更精确地实现麻醉深度监护。最后展望了今后其应用于麻醉深度监测领域的发展前景。

Depth of anesthesia (DOA) monitoring is extremely important in improving the quality of anesthesia, ensuring patient’s safety and rehabilitation in operation. Electroencephalogram (EEG) signal analysis algorithms are reviewed systematically. The application research and development state of these parameters algorithms such as bispectral index scale (BIS), nacrotrend (NT) and cerebral state index (CSI) in DOA monitoring process are introduced. Firstly, the advantages and disadvantages of EEG analysis algorithms of these parameters in clinical application are compared. Secondly, the sort entropy index (SEI) algorithm with excellent de-nosing capability is proposed. And the back propagation (BP) neural network is used to revise and optimize BIS, NT and SEI to implement DOA monitoring more accurately. Finally, the applications in DOA monitoring field are forecasted.