Advanced EEG Signal Processing in Brain Death Diagnosis


In this chapter, we present several electroencephalography (EEG) signal processing and statistical analysis methods for the purpose of clinical diagnosis of brain death, in which an EEG-based preliminary examination system was developed during the standard clinical procedure. Specifically, given the reallife recorded EEG signals, a robust principal factor analysis (PFA) associated with independent component analysis (ICA) approach is applied to reduce the power of additive noise and to further separate the brain waves and interference signals. We also propose a few frequency-based and complexity-based statistics for quantitative EEG analysis with an aim to evaluate the statistical significance differences between the coma patients and quasi-brain-death patients. Based on feature selection and classification, the system may yield a binary decision from the classifier with regard to the patient's status. Our empirical data analysis has shown some promising directions for real-time EEG analysis in clinical practice.


Independent Component Analysis Independent Component Analysis Independent Component Analysis Algorithm Principal Factor Analysis Standard Principal Component Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Akay, M. (ed.): Nonlinear Biomedical Signal Processing, vol. II Dynamical Analysis and Modeling. IEEE Press, New York (2001)Google Scholar
  2. 2.
    Amari, S.: Natural gradient works efficiently in learning. Neural Computation 10, 251–276 (1998)CrossRefGoogle Scholar
  3. 3.
    Amari, S.: Natural gradient for over- and under-complete bases in ICA. Neural Computation 11(8), 1875–1883 (1999)CrossRefGoogle Scholar
  4. 4.
    Amari, S., Cichocki, A., Yang, H.: Advances in Neural Information Processing Systems, vol. 8, chap. A new learning algorithm for blind signal separation, pp. 757–763. MIT, Cambridge, MA (1996)Google Scholar
  5. 5.
    Cao, J.: Lecture Notes in Artificial Intelligence, vol. 3973, chap. Analysis of the quasi-brain-death EEG data based on a robust ICA approach, pp. 1240–1247. Springer, Berlin Heidelberg New York (2006)Google Scholar
  6. 6.
    Cao, J., Murata, N., Amari, S., Cichocki, A., Takeda, T.: Independent component analysis for unaveraged single-trial MEG data decomposition and single-dipole source localization. Neurocomputing 49, 255–277 (2002)CrossRefGoogle Scholar
  7. 7.
    Cao, J., Murata, N., Amari, S., Cichocki, A., Takeda, T.: A robust approach to independent component analysis with high-level noise measurements. IEEE Transactions on Neural Networks 14(3), 631–645 (2003)CrossRefGoogle Scholar
  8. 8.
    Cardoso, J., Laheld, B.: Equivariant adaptive source separation. IEEE Transactions on Signal Processing 44, 3017–3030 (1996)CrossRefGoogle Scholar
  9. 9.
    Cardoso, J., Souloumiac, A.: Jacobi angles for simultaneous diagonalization. SIAM Journal of Matrix Analysis and Applications 17, 145–151 (1996)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Carmeli, G., Knyazeva, G., Innocenti, G., Feo, O.: Assessment of EEG synchronization based on state-space analysis. NeuroImage 25, 330–354 (2005)CrossRefGoogle Scholar
  11. 11.
    Chen, F., Xu, J., Gu, F., Yu, X., Meng, X., Qiu, Z.: Dynamic process of information transmission complexity in human brains. Biological Cybernetics 83, 355–366 (2000)CrossRefGoogle Scholar
  12. 12.
    Chen, Z., Cao, J.: An empirical quantitative EEG analysis for evaluating clinical brain death. In: Processings of the 2007 IEEE Engineering in Medicine and Biology 29th Annual Conference, pp. 3880–3883. Lyon, France (2007)CrossRefGoogle Scholar
  13. 13.
    Chen, Z., Cao, J., Cao, Y., Zhang, Y., Gu, F., Zhu, G., Hong, Z., Wang, B., Cichocki, A.: An empirical EEG analysis in brain death diagnosis for adults. Cognitive Neurodynamics (under review)Google Scholar
  14. 14.
    Eelco, F., Wijdicks, M.: Brain death worldwide. Neurology 58, 20–25 (2002)Google Scholar
  15. 15.
    Gu, F., Meng, X., Shen, E.: Can we measure consciousness with EEG complexities? International Journal of Bifurcation and Chaos 13(3), 733–742 (2003)zbMATHCrossRefGoogle Scholar
  16. 16.
    Hyv\ddot{a}rinen, A., Oja, E.: A fast fixed-point algorithm for independent component analysis. Neural Computation 9, 1483–1492 (1997)Google Scholar
  17. 17.
    Lee, T., Girolami, M., Sejnowski, T.: Independent component analysis using an extended infomax algorithm for mixed sub-gaussian and super-gaussian sources. Neural Computation 11, 417–441 (1998)CrossRefGoogle Scholar
  18. 18.
    Lin, M., Chan, H., Fang, S.: Linear and nonlinear EEG indexes in relation to the severity of coma. In: Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, vol. 5, pp. 4580–4583 (2005)Google Scholar
  19. 19.
    Makeig, S., Bell, A., Jung, T., Sejnowski, T.: Advances in Neural Information Processing System, vol. 8, chap. Independent component analysis of electroencephalographic data, pp. 145–151. MIT, Cambridge, MA (1996)Google Scholar
  20. 20.
    Marks, S., Zisfein, J.: Apneic oxygenation in apnea tests for brain death: a controlled trial. Neurology 47, 300–303 (1990)Google Scholar
  21. 21.
    Molgedey, L., Schuster, H.: Separation of a mixtures of independent signals using time delayed correlations. Physical Review Letters 72(23), 3634–3637 (1994)CrossRefGoogle Scholar
  22. 22.
    Niedermeyer, E.: Electroencephalography: Basic Principles, Clinical Applications, and Related Fields. Lippoincott Williams & Wilkins, Baltimore, MD (1991)Google Scholar
  23. 23.
    Peng, C., Buldyrev, S., Havlin, S., Simons, M., Stanleyn, H., Goldberger, A.: Mosaic organization of DNA nucleotides. Physical Review E 49, 1685–1689 (1994)CrossRefGoogle Scholar
  24. 24.
    Pincus, S.: Approximate entropy (apen) as a complexity measure. Proceedings of National Academy of Science 88, 110–117 (1991)CrossRefMathSciNetGoogle Scholar
  25. 25.
    Roberts, J., Penny, W., Rezek, I.: Temporal and spatial complexity measures for EEG-based brain–computer interfacing. Medical and Biological Engineering and Computing 37(1), 93–99 (1998)CrossRefGoogle Scholar
  26. 26.
    Schölkopf, B., Smola, A.: Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond. MIT, Cambridge, MA (2002)Google Scholar
  27. 27.
    Tanaka, T., Mandic, D.: Complex empirical mode decomposition. IEEE Signal Processing Letters 14, 101–104 (2007)CrossRefGoogle Scholar
  28. 28.
    Taylor, R.: Reexamining the definition and criteria of death. Seminars in Neurology 17, 265–270 (1997)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  1. 1.Saitama Institute of TechnologyJapan
  2. 2.Massachusetts General HospitalBostonUSA

Personalised recommendations