基于小波变换的动态脑电节律提取

针对脑电信号和其他医学信号的非平稳性，引入小波变换处理临床脑电信号的动态特性。根据脑电信号的不同节律特性，提出应用小波包变换构造不同频率特性的滤波器，提取脑电信号的４种节律，并由各种节律对应的小波系数构造动态脑电地形图。为了研究不同脑功能状态下脑电信号４种节律的动态特性，文中对两组不同临床脑电数据进行分析与比较，给出了有关的实际分析结果。实验结果表明，利用小波包分析的滤波特性，能够有效地反映临床脑电不同节律的动态特性，也为分析其他生物医学信号提供了一条新的途径。

Detection of Dynamic EEG Rhythms Based on Wavelet Transformation

Wavelet transformation is employed to investigate the nonstationarity of clinical EEG signals and other medical signals. On the basis of the property of different EEG rhythms, wavelet packet analysis is used for designing filters with different frequency characteristics to detect 4 kinds of EEG rhythms. The coefficients corresponding to the rhythms are used to form the dynamic electrical brain activity mapping (DBEAM). In order to examine the dynamic characteristics of 4 basic rhythms under different functional states of brain, two kinds of clinical EEG data with different brain function states are analyzed and compared. Some useful results are also provided and the experimental results indicate that the dynamic characteristics of clinical brain electrical activities can be demonstrated by using wavelet packet technique. The method also proposes a new way for the analysis of other biomedical signals.