An empirical EEG analysis in brain death diagnosis for adults

Abstract

Electroencephalogram (EEG) is often used in the confirmatory test for brain death diagnosis in clinical practice. Because EEG recording and monitoring is relatively safe for the patients in deep coma, it is believed to be valuable for either reducing the risk of brain death diagnosis (while comparing other tests such as the apnea) or preventing mistaken diagnosis. The objective of this paper is to study several statistical methods for quantitative EEG analysis in order to help bedside or ambulatory monitoring or diagnosis. We apply signal processing and quantitative statistical analysis for the EEG recordings of 32 adult patients. For EEG signal processing, independent component analysis (ICA) was applied to separate the independent source components, followed by Fourier and time-frequency analysis. For quantitative EEG analysis, we apply several statistical complexity measures to the EEG signals and evaluate the differences between two groups of patients: the subjects in deep coma, and the subjects who were categorized as brain death. We report statistically significant differences of quantitative statistics with real-life EEG recordings in such a clinical study, and we also present interpretation and discussions on the preliminary experimental results.

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Notes

  1. 1.

    Nevertheless, it was pointed out in Wijdicks (1995) that most patients meeting the clinical criterion for brain death might still have isoelectric EEGs (≤2 μV at a sensitivity of 2 μV/mm).

  2. 2.

    The CBF test involves the injection of a mild radioactive isotope into the blood stream. By placing a radioactivity counter over the head, one can measure the amount of blood flow into the brain. The cerebral blood flow study takes 20–30 min to perform. If there is no blood flow to the brain as demonstrated by this study, the brain is dead. A negative cerebral flow study is indisputable evidence of a dead brain.

  3. 3.

    The layout of the electrodes on the frontal regions of the brain is simply for the technical convenience without interfering with other medical treatment or moving the patient’s body. However, the EEG confirmatory test conducted at later stage (see the flowchart of Fig. 1) will require the electrodes cover the whole scalp.

  4. 4.

    Indeed, the presence of the slow activity (< 4 Hz) was always found in all measurements, which created a difficulty for discrimination.

  5. 5.

    All of quantitative analysis softwares were written in MATLAB© (MathWorks, Natick, MA) and are available from the authors upon request.

References

  1. Ad hoc committee of the Harvard medical school to examine the definition of brain death (1968) A definition of irreversible coma. JAMA 205:337–340

    Google Scholar 

  2. Akay M (ed) (2001) Nonlinear biomedical signal processing, vol. II. Dynamical analysis and modeling. IEEE Press, New York

    Google Scholar 

  3. Buchner H, Schuchardt V (1990) Reliability of electroencephalogram in the diagnosis of brain death. Eur Neurol 30(3):138–141

    PubMed  Article  CAS  Google Scholar 

  4. Calhoun VD, Adali T, Pearlson GD, van Zijl PCM, Pekar JJ (2002) Independent component analysis of fMRI data in the complex domain. Magn Reson Med 48:180–192

    PubMed  Article  CAS  Google Scholar 

  5. Cao J (2006) Analysis of the quasi-brain-death EEG data based on a robust ICA approach. In: Proc. 10th Int. Conf. Knowledge-Based & Intelligent Information & Engineering Systems, Bournemouth, UK, 2006 (Lecture Notes in Computer Science 4253, pp 1240–1247)

  6. Cao J, Murata N, Amari S, Cichocki A, Takeda T (2002) Independent component analysis for unaveraged single-trial MEG data decomposition and single-dipole source localization. Neurocomputing 49:255–277

    Article  Google Scholar 

  7. Cao J, Murata N, Amari S, Cichocki A, Takeda T (2003) A robust approach to independent component analysis of signals with high-level noise measurements. IEEE Trans Neural Netw 14:631–645

    PubMed  Article  Google Scholar 

  8. Chen Z, Cao J (2007) An empirical quantitative EEG analysis for evaluating clinical brain death. In: Proc. IEEE 29th annual conf. engineering in medicine and biology (EMBC’07). Lyon, France, pp 3880–3883

  9. Chen F, Xu JH, Gu FJ, Yu XH, Meng, Qiu ZC (2000) Dynamic process of information transmission complexity in human brains. Biol Cybern 83:355–366

    PubMed  Article  CAS  Google Scholar 

  10. Chen Z, Ohara S, Cao J, Vialatte F, Lenz FA, Cichocki A (2007) Statistical modeling and analysis of laser-evoked potentials of electrocorticogram recordings from awake humans. Comput Intell Neurosci, vol 2007, Article ID 10479

  11. Cichocki A, Amari S (2002) Adaptive blind signal and image processing. Wiley, New York

    Google Scholar 

  12. Cohen L (1995) Time-frequency analysis. Prentice Hall, Englewood Cliffs, NJ

    Google Scholar 

  13. Dietterich TG, Bakiri G (1995) Solving multiclass learning problems via error-correcting output codes. J Artif Intell Res 2:263–286

    Google Scholar 

  14. Goldberger AL, Amaral LAN, Hausdorff JM, Ivanov P, Peng CK, Stanley HE (2002) Fractal dynamics in physiology: alterations with disease and aging. Proc Natl Acad Sci USA 99(suppl 1):2466–2472

    PubMed  Article  Google Scholar 

  15. Gonzalez Andino SL, Grave de Peralta Menendez R, Thut G, Spinelli L, Blanke O, Michel CM, Seeck M, Landis T (2000) Measuring the complexity of time series: an application to neurophysiological signals. Hum Brain Mapp 11(1):46–57

    PubMed  Article  CAS  Google Scholar 

  16. Gu FJ, Meng X, Shen E (2003) Can we measure consciousness with EEG complexities? Int J Bifurc Chaos 13(3):733–742

    Article  Google Scholar 

  17. Hornero R, Abásolo D, Jimeno N, Sánchez CI, Poza J, Aboy M (2006) Variability, regularity, and complexity of time series generated by schizophrenic patients and control subjects. IEEE Trans Biomed Eng 53(2):210–217

    PubMed  Article  Google Scholar 

  18. Kaspar F, Schuster HG (1987) Easily calculable measure for the complexity of spatiotemporal patterns. Phys Rev A36:842–848

    PubMed  Article  Google Scholar 

  19. Lin M, Chan H, Fang S (2005) Linear and nonlinear EEG indexes in relation to the severity of coma. In: Proc. IEEE EMBC’05, pp 4580–4583

  20. Litscher G (1999) New biomedical devices and documentation of brain death. Internet J Anesthesiol 3(4)

  21. Little M, McSharry P, Moroz I, Roberts S (2006) Nonlinear, biophysically-informed speech pathology detection. In: Proc. ICASSP’06. Toulouse, France, pp 1080–1083

  22. Makeig S, Westerfield M, Jung TP, Enghoff S, Townsend J, Courchesne E, Sejnowski TJ (2002) Dynamic brain sources of visual evoked responses. Science. 295:690–694

    PubMed  Article  CAS  Google Scholar 

  23. Niedermeyer E (ed) (1991) Coma and brain death. In: Electroencephalography: basic principles, clinical applications, and related fields, Chap. 26. Lippoincott Williams & Wilkins, Baltimore, MD

  24. Pallis C, MacGillivray B (1980) Brain death and the EEG. Lancet 316:1085–1086

    Article  Google Scholar 

  25. Papadelis C, Chen Z, Kourtidou-Papadeli C, Bamidis PD, Bekiaris A, Maglaveras N (2007) Monitoring sleepiness with on-board electrophysiological recordings for preventing sleep-deprived traffic accidents. Clin Neurophysiol 118(9):1906–1922

    PubMed  Article  Google Scholar 

  26. Peng CK, Buldyrev SV, Havlin S, Simons M, Stanley HE, Goldberger AL (1994) Mosaic organization of DNA nucleotides. Phys Rev E 49:1685–1689

    Article  CAS  Google Scholar 

  27. Pincus SM (1991) Approximate entropy (ApEn) as a complexity measure. Proc Natl Acad Sci USA 88:110–117

    Article  Google Scholar 

  28. Pockett S, Whalen S, McPhail AVH, Freeman WJ (2007) Topography, independent component analysis and dipole source analysis of movement related potentials. Cogn Neurodyn 1(4):327–340

    Article  PubMed  Google Scholar 

  29. Roberts J, Penny WD, Rezek I (1998) Temporal and spatial complexity measures for EEG-based brain-computer interfacing. Med Biol Eng Comput 37(1):93–99

    Article  Google Scholar 

  30. Schneider S (1989) Usefulness of EEG in the evaluation of brain death in children: the cons. Electroencephalogr Clin Neurophysiol 73(4):276–278

    PubMed  Article  CAS  Google Scholar 

  31. Schölkopf B, Smola AJ (2002) Learning with kernels: support vector machines, regularization, optimization and beyond. MIT Press, Cambridge MA

    Google Scholar 

  32. Schölkopf B, Smola AJ, Müller KR (1998) Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput 10:1299–1319

    Article  Google Scholar 

  33. Taylor RM (1997) Reexamining the definition and criteria of death. Semin Neurol 17:265–270

    PubMed  CAS  Article  Google Scholar 

  34. Wennervirta J, Salmi T, Hynynen M, Yli-Hankala A, Koivusalo A-M, Van Gils M, Pöyhiä R, Vakkuri A (2007) Entropy is more resistant to artifacts than bispectral index in brain-dead organ donors. J Intensive Care Med 33(1):133–136

    Article  Google Scholar 

  35. Wijdicks EFM (1995) Determining brain death in adults. Neurology 45:1003–1011

    PubMed  CAS  Google Scholar 

  36. Wijdicks EFM (2002) Brain death worldwide: accepted fact but no global consensus in diagnostic criteria. Neurology 58:20–25

    PubMed  Article  Google Scholar 

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Acknowledgements

This work was supported in part by the Japan Society for the Promotion Science (JSPS) and the National Natural Science Foundation of China (NSFC) in the Japan-China Research Cooperative Program. We thank two anonymous reviewers for many valuable comments.

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Correspondence to Zhe Chen.

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Chen, Z., Cao, J., Cao, Y. et al. An empirical EEG analysis in brain death diagnosis for adults. Cogn Neurodyn 2, 257 (2008). https://doi.org/10.1007/s11571-008-9047-z

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Keywords

  • Brain death
  • Quantitative EEG
  • Independent component analysis
  • Approximate entropy
  • Detrended fluctuation analysis
  • Pattern classification