基于改进功率谱熵的抑郁症脑电信号活跃性研究

Activity analysis of depression electroencephalogram based on modified power spectral entropy

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.

A method is proposed to calculate and analyze electro-encephalogram signal to improve the situation that there is an urgent need for an effective quantitative indicator to describe brain mental disorders. The method defines a spectral entropy in terms of the power spectrum division of time series. Then, the entropy is applied to numerical calculation of electroencephalogram signals of depression patients and normal control group. Meanwhile, the differences are compared between them. Experimental results show that the power spectral entropy in depression patients is significantly weaker than the normal healthy people＇s in some brain regions. Further analysis proves two facts. One is that the entropy is positively correlated to brain electrical physiological activity, and the other tells that the entropy can be used as a parameter to measure brain electrical activity, to characterize brain electrical physiological activities, and to provide the activity intensity information. This paper determines that the power spectral entropy for electroencephalogram plays an important role in diagnosis of brain mental disorder.