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A method of identifying chronic stress by EEG

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Abstract

There are a lot of studies on chronic stress assessment applying psychology instruments or hormones analysis. However, there are only few studies using electroencephalogram (EEG), which is a non-invasive method providing objective inspection on brain functioning. In this paper, we analyzed overall complexity and spectrum power of certain EEG bands (theta, alpha and beta) collected from two groups of human subjects—high stress versus moderate stress at prefrontal sites (Fp1, Fp2 and Fpz). The results showed that the differences of nonlinear features (C0, LZC, D2, L1 and RE) and linear features (power and alpha asymmetry score) between two groups are significant. C0, LZC and D2 significantly increased in stress group at Fp1 and Fp2, while L1 and RE significantly decreased. And those with chronic stress have higher left prefrontal power. Finally, we suggest that it may be effective to discriminate the high-stress people from moderate-stress people by EEG.

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References

  1. Heim C, Nemeroff CB (2002) Neurobiology of early life stress: clinical studies. Semin Clin Neuropsychiatry 7(2):147–159

    Article  Google Scholar 

  2. Schneiderman N, Gail Ironson G, Siegel SD (2005) Stress and health: psychological, behavioral and biological determinant. Annu Rev Clin Psychol 1:607–628

    Article  Google Scholar 

  3. van Praag HM (2004) Can stress cause depression? Prog Neuropsychopharmacol Biol Psychiatry 28(5):891–907

    Article  Google Scholar 

  4. Cate Miller A, Gordon RM, Daniele RJ, Diller L (1992) Stress, appraisal, and coping in mothers of disabled and nondisabled children. J Pediatr Psychol 17(5):587–605

    Article  Google Scholar 

  5. Nunez PL (2006) Electric fields of the brain, 2nd edn. Oxford University Press, USA

    Book  Google Scholar 

  6. Thakor NV, Tong S (2004) Advances in quantitative electroencephalogram analysis methods. Annu Rev Biomed Eng 6:453–495

    Article  Google Scholar 

  7. Stam CJ (2005) Nonliear dynamical analysis of EEG and EMG: review of an emerging field. Clin Neurophysiol 116(10):2266–2301

    Article  Google Scholar 

  8. Hammen C (2005) Stress and depression. Ann Rev Clin Psychol 1:293–319

    Article  Google Scholar 

  9. McGonagle KA, Kessler RC (1990) Chronic stress, acute stress, and depressive symptoms. Am J Commun Psychol 18(5):681–706

    Article  Google Scholar 

  10. Blackhart GC, Minnix JA, Kline JP (2006) Can EEG asymmetry patterns predict future development of anxiety and depression? A preliminary study. Biol Psychol 72(1):46–50

    Article  Google Scholar 

  11. Kemp AH, Griffiths K, Felmingham KL, Shankman SA, Drinkenburg W, Arns M, Clark CR, Bryant RA (2010) Disorder specificity despite comorbidity: resting EEG alpha asymmetry in major depressive disorder and post-traumatic stress disorder. Biol Psychol 85(2):350–354

    Article  Google Scholar 

  12. Li Y, Tong S, Liu D, Gai Y, Wang X, Wang J, Qiu Y, Zhu Y (2008) Abnormal EEG complexity in patients with schizophrenia and depression. Clin Neurophysiol 119(6):1232–1241

    Article  Google Scholar 

  13. Davidson RJ (1995) Cerebral asymmetry, emotion, and affective style. In: Davidson RJ, Hugdahl K (eds) Brain asymmetry. MIT Press, Cambridge, pp 361–387

    Google Scholar 

  14. Chen F, Gu F, Xu J, Liu Z, Liu R (1998) A new measurement of complexity for studying EEG mutual information. Biophys Sinica 14(3):435–437

    Google Scholar 

  15. Cai Z, Sun H (2008) Improved C0-complexity and its applications. J Fudan Univ 47(6):133–140

    Google Scholar 

  16. Lempel A, Ziv J (1976) On the complexity of finite sequences. Inf Theory 22(1):75–81

    Article  MathSciNet  MATH  Google Scholar 

  17. Nagarajan R, Szczepanski J, Wajnryb E (2008) Interpreting non-random signatures in biomedical signals with Lempel–Ziv complexity. Phys D Nonlinear Phenom 237(3):359–364

    Article  MathSciNet  Google Scholar 

  18. Stam CJ, van Woerkom TCAM, Pritchard WS (1996) Use of non-linear EEG measures to characterize EEG changes during mental activity. Electroencephalogr Clin Neurophysiol 99(3):214–224

    Article  Google Scholar 

  19. Lee Y-J, Zhu Y-S, Xu Y-H, Shen M-F, Zhang H-X, Thakor NV (2001) Detection of non-linearity in the EEG of schizophrenic patients. Clin Neurophysiol 112(7):1288–1294

    Article  Google Scholar 

  20. Kulish V, Sourin A, Sourina O (2006) Human electroencephalograms seen as fractal time series: mathematical analysis and visualization. Comput Biol Med 36(3):291–302

    Article  Google Scholar 

  21. Rosenstein MT, Collins JJ, De Luca CJ (1993) A practical method for calculating Largest Lyapunov exponents from small data sets. Phys D Nonlinear Phenom 65(1–2):117–134

    Article  MATH  Google Scholar 

  22. Tang Y, Li Y, Tong S, Li Y, Zhu Y (2009) Entroy analysis of the EEG alpha activity in depression patients. J Biomed Eng 26(4):739–742

    Google Scholar 

Download references

Acknowledgments

This work was supported by the National Basic Research Program of China (973 Program) (No. 2011CB711001), the National Natural Science Foundation of China (grant No. 60973138), the EU’s Seventh Framework Programme OPTIMI (grant No. 248544), the Fundamental Research Funds for the Central Universities (grant No. lzujbky-2009-62), the Interdisciplinary Innovation Research Fund For Young Scholars of Lanzhou University (grant No. LZUJC200910).

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Correspondence to Bin Hu.

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Peng, H., Hu, B., Zheng, F. et al. A method of identifying chronic stress by EEG. Pers Ubiquit Comput 17, 1341–1347 (2013). https://doi.org/10.1007/s00779-012-0593-3

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  • DOI: https://doi.org/10.1007/s00779-012-0593-3

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