复杂度脑电地形图研究

脑电地形图是近年脑电分析的热点之一。通过对各种复杂度算法的分析得出，近似熵由于所需要的时间序列长度较短，大大减少了脑电非平稳性所带来的困难，且无需粗粒化，在对生物医学信号的复杂度分析中有其一定的优点，采用近似熵对多道脑电信号的复杂度运算结果，通过空间插值，构建复杂性动态脑地形图，以便于观察大脑各部ＥＥＧ信号复杂度在同一时刻的相对强弱关系和这种关系随时间的变化。并通过对一些脑疾病患者脑电数据的分析，

EEG COMPLEXITY TOPOGRAPHY

EEG topography is one of the hotspots in EEG analysis. Through analysis and comparison of several kinds of complexity measure algorithm, we found that approximate entropy needs shorter time series so that some difficulty owing to the nonstationarity could be overcome; in addition, no coarse graining preprocessing is needed. Therefore it has some virtues in complexity analysis of biomedical signals. First we compute the complexity measures for several channels of EEG signals, then through interpolation, construct dynamic complexity topography so as to observe relative intensities among different parts of EEG signal complexity at the same time and their changes with time. Through analysis of some patients' EEG data, we explored possible difference between abnormal and normal subjects in complexity topography, from which some information for diagnosing brain disease especially for some functional disease was extrected. We found that topography pattern of schizophrenia patient with eye closed was more complex than that of normal subjects. We also found the complexity level would decrease during epileptic seizure capture.