采用非线性动力学方法研究脑精神疾病是近年来国内外学者研究的热点和趋势.针对脑精神疾病的研究和诊断中缺少客观有效的量化参数和量化指标的状况,提出了一种根据对时间序列功率谱划分而定义的谱熵,然后用其计算和分析脑电信号谱熵的方法.通过数据仿真试验证明该谱熵和信号活跃性之间存在正相关关系.基于这种相关性,应用该方法对抑郁症患者和正常对照组的脑电信号功率谱熵进行了数值计算,然后进行了分析对比和统计检验.实验结果表明:抑郁症患者脑电信号的功率谱熵在部分脑区显著弱于正常健康人.证明该谱熵能够表征大脑电生理活动状况,提供反映其活动性强弱的信息,可以作为度量大脑电生理活动性的一个参数.这对于能否将该功率谱熵作为诊断脑精神疾病的物理参数具有积极意义. Nonlinear dynamics study of mental illness is one of the hot spots and trends in recent years both at home and abroad.Aiming at the lack of objective and effective quantitative parameters and quantitative indicators in the research and diagnosis of brain mental diseases, And the spectrum entropy defined by the sequence power spectrum partition, and then use it to calculate and analyze the spectral entropy of the EEG signal.Through the data simulation test, it is proved that there is a positive correlation between the spectral entropy and the signal activity.Based on this correlation, The power spectral entropy of EEG signals in patients with depression and normal controls was numerically calculated and then analyzed and compared with statistical tests.The experimental results show that the power spectrum entropy of EEG in patients with depression is significant in some brain regions Weaker than normal healthy people.It is proved that this spectral entropy can characterize the state of electrophysiological activity of the brain and provide the information that reflects the activity of the brain.It can be used as a parameter to measure the electrophysiological activity of the brain.It can be used as the entropy of the power spectrum It is of positive significance to diagnose the physical parameters of brain mental illness.