小波变换在麻醉监测诱发脑电信号分类中的应用  

Classification of Evoked Potential Signal During Anesthesia Monitoring with Wavelet Transform

作　　者：张烈平[1] 莫玮[2] 牛秦洲[1] 

机构地区：[1]桂林工学院电子与计算机系,桂林541004 [2]桂林电子工业学院,桂林541004

出　　处：《计算机工程与应用》2003年第20期198-200,共3页Computer Engineering and Applications

基　　金：国家自然科学基金资助项目(编号:69871010);桂林工学院青年扶持基金资助

摘　　要：采用小波变换方法对麻醉监测脑电信号进行分析,通过基于小波分解的能量特征提取方法提取麻醉状态下中潜伏期听觉诱发脑电的特征,并用BP网络分类器对提取的特征进行聚类,从而实现麻醉深度的估计,实验仿真结果表明了该方法的有效性。An energy feature extraction method to evoked potential signal during anesthesia monitoring based on wavelet transform is presented in this paper.The energy features extracted from MLAEP during anesthesia are classified with a BP neural network for estimating anesthesia depth.Experimental simulation results show that the method is effective.

关 键 词：小波变换 MLAEP 特征提取 BP网络 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]