脑电图复杂度分析中的粗粒化问题 I.过分粗粒化和三种复杂度的比较

在对生物医学信号时间序列进行复杂度分析时，往往需要首先对原始信号进行粗粒化预处理。这种预处理有可能会造成丢失原始信号中所蕴含的信息，甚至在某些情况下根本改变原信号的动力学性质。本文提出了克服这一问题的一些途径，通过对若干种复杂度定义的比较研究，建议采用近似熵和我们所定义的Ｃ０复杂度作为复杂性度量。并以脑电的复杂性分析为例作了说明。

COARSE GRAINING IN COMPLEXITY ANALYSIS OF EEGI. OVER COARSE GRAINING AND A COMPARISON AMONG THREE KINDS OF COMPLEXITIES

It is often to take a coarse graining preprocessing before a complexity analysis is made for biomedical signal time series. Some information may be lost during such preprocessing, and even the dynamic property of the original signal may be radically distorted in some case. Some approaches to overcome such problems were proposed in this article. Comparing different complexity measures, we suggest that approximate entropy and C0 complexity defined by us may be proper candidates for such analysis. Complexity analysis of EEG was done as examples.