Abstract: Wavelet entropy is adopted to analyze the difference of spontaneous EEG complexity between the melancholia and normal under the states of quiet and mental arithmetic tasks. It collects the 16-channel EEG data from 10 melancholia and 10 normal under the two states： a resting state with eyes closed and a mental arithmetic state with eyes closed, and then it calculates the wavelet entropy of the four group EEG data after noise reduction. The result shows that, the wavelet entropy of spontaneous EEG has a significant difference between melancholia and normal： （1）in the same state, the wavelet entropy of the melancholia’s each channel is greater than the corresponding normal’s; （2）for the same person, the wavelet entropy of each channel under the state of a resting condition with eyes closed is greater than that under the state of a mental arithmetic with eyes closed. The results show the complexity of EEG data under a resting condition with eyes closed is higher than that under a mental arithmetic with eyes closed, and the complexity of the melancholia’s EEG signal is higher than that of the healthy persons. The results are important for the diagnosis of melancholia.

Key words: melancholia, wavelet entropy, Electroencephalography（EEG）, complexity

摘要： 应用小波熵理论分析抑郁症患者和健康人在安静和心算任务下自发脑电信号的复杂度：分别采集10例抑郁症患者和10例正常人在安静闭目和闭眼心算连减两种状态下的16导联脑电信号；计算这四组脑电数据的小波熵，并进行对比和统计分析。结果表明，抑郁症患者和正常人自发脑电的小波熵有着显著的差异：（1）在相同状态下，抑郁症患者各导联脑电的小波熵大于正常人对应导联的小波熵；（2）对同一个人，安静闭目状态下各导联脑电的小波熵大于心算连减状态下对应导联的小波熵。结论可为抑郁症的诊断提供参考。

关键词: 抑郁症, 小波熵, 脑电, 复杂度