基于小波变换的麻醉监测脑电信号的分析与处理

Analysis and processing of anesthesia monitoring of EEG based on wavelet transformation

针对中潜伏期听觉诱发脑电的特点 ,提出了利用小波变换的多分辨分析技术滤除被测信号的强噪声成分 ,重构真实信号来实现对中潜伏期听觉诱发脑电的提取方法 ;通过实验仿真表明 :小波变换提取技术比其他传统提取方法更有效 ,可以减少试验次数 ,可以提供更为可靠的特征提取和模式识别的分析数据 。

The principle of wavelet transformation,multiresolution analysis and Mallat algorithm are introduced in this paper.According to the characters of MLAEP, the extraction method is proposed to denoise and reconstruct the MLEAP signal from strong noise background. The simulation results show that the extraction method based on wavelet transformation is more effective than other traditional methods.It can considerally reduce the tests,provide more useful analysis data for feature extraction and mode identification,and offer an important theory basis for clinic anesthesia monitoring.