Analysis of EEG in Melancholia Based on Wavelet Entropy and Complexity

The difference of EEG complexity between normal and melancholic subjects is analyzed, which tries to reveal the characteristics of melancholic's EEG complexity. In this paper, 16-channel EEG data are recorded in 10 melancholic and 10 healthy persons under two states: a resting condition with eyes closed, a mental arithmetic with eyes closed. And then the wavelet entropy method and the complexity are applied to analyze the EEG. The results show that, the wavelet entropy value has a significant difference (P<;0.05) between melancholic and healthy persons under two states, and they also prove that the characteristics of the wavelet entropy, that is, the more complex the signals, the greater the wavelet entropy value. Meanwhile, the complexity of the melancholic's EEG signal is obviously higher than healthy persons, however the spatial distributions of complexity is similar under two states. These methods can effectively detect the dynamic complexity of EEG, and have provided the auxiliary objective basis in the diagnosis and detection of melancholia.