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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 ,探索病理情况下复杂性脑地形图与正常对照在结构上的不同,从中提取信息 ,以期获得一种客观且易于为临床接受的对脑疾患、特别是功能性疾患的判别方法。通过分析发现精神分裂患者闭眼时的复杂度脑地形图模式较正常人复杂 ,而癫痫患者在癫痫发作期脑电复杂度水平降低。
EEG topographic map is one of the hot spots in recent years. Through the analysis of various complexity algorithms, approximate entropy due to the shorter length of the required time series, greatly reducing the EEG non-stationary difficulties, and without the need for coarse graining, in the biomedical signal Complexity analysis has its own advantages. By using the approximate entropy to calculate the complexity of multi-channel EEG signals, the complex dynamic brain topography map is constructed by spatial interpolation to observe the relative strength and weakness of EEG signal complexity in different parts of the brain at a same time. The change. Through analyzing the EEG data of some patients with brain diseases and exploring the structural differences between the complicated brain topography and the normal controls under pathological conditions, the information is extracted from them so as to obtain an objective and easily accepted clinical response to brain disorders, In particular, the identification of functional disorders. Analysis found that patients with schizophrenia complicated by closed eyes when the brain topography pattern more complex than normal, and epileptic patients in epileptic seizure EEG level decreased.