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The Research of Depression Based on Power Spectrum

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Book cover Brain Informatics and Health (BIH 2015)

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Abstract

Depression is a common emotional disease endangering human health [1], however, there is no objective and precise method for the diagnosis of depression at present both at home and overseas. Through power spectrum analysis for two leads EEG signals of the depression group and the control group, we extracted the relative power and gravity frequency, two characteristic parameters of the EEG signals each rhythm, and studied the differences between the two groups’ characteristic parameters. The results of SPSS statistical analysis demonstrate that the EEG characteristic parameters have a strong correlation with the degree of depression. Specifically speaking, the relative power of depression group’s left and right brain \(\alpha \) rhythm is asymmetric, and the relative power of depression group’s \(\delta \) rhythm is higher than the control group, while the relative power of \(\alpha \) rhythm is lower than the control group. In addition, the gravity frequency of depression group migrates to the low frequency compared with the control group. Relative power and gravity frequency reflect the suppressive state of the body objectively and accurately, compared with the clinical diagnosis method and the rating scale, can be more objective diagnosis of depression.

Supported by the National Basic Research Program of China (973 Program) (No.2014CB744600, No.2011CB711000), the Program of International S&T Cooperation of MOST (No.2013DFA11140), the National Natural Science Foundation of China (grant No.61210010, No.61300231), and Natural Science Foundation of Gansu Province, China (1208RJZA127), Central Universities Fundamental Research Funds (Grant No.lzujbky-2015-k07).

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Correspondence to Bin Hu .

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Sun, MH., Zhao, QL., Hu, B., Chen, Y., Xu, LJ., Peng, H. (2015). The Research of Depression Based on Power Spectrum. In: Guo, Y., Friston, K., Aldo, F., Hill, S., Peng, H. (eds) Brain Informatics and Health. BIH 2015. Lecture Notes in Computer Science(), vol 9250. Springer, Cham. https://doi.org/10.1007/978-3-319-23344-4_39

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  • DOI: https://doi.org/10.1007/978-3-319-23344-4_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23343-7

  • Online ISBN: 978-3-319-23344-4

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