EEG Analysis for Differentiating between Brain Death and Coma in Humans
Keywords:Brain death, Coma, EEG analysis, Signal Complexity, Non-parameter Test.
To have an accuracy and fast diagnosis of brain death is very urgent for patients especially for distinguishing brain death from coma circumstance. Electroencephalogram is a method of invasive recording brain signals with high temporal resolution. So in clinics, EEG is taken as the most reliable method to estimate the brain death state of affairs. The objective of this paper is to find out the statistical rule on brain death and coma detection through EEG signal processing technology analysis on 20 patientsâ€™ medical data (10 for brain death group, 10 for coma group) and then hope to give aid diagnosis conclusions in clinical practice. First, the independent component analysis (ICA) has been applied to solve artifact removal problem combined with wavelet decomposition method to compute the signal noise ratio (SNR). Moreover, the Fourier transform is calculated to compare the power spectrum of the two groups. In addition, we applied the multi-scale permutation entropy into comparison with complexity of brain death and coma patientsâ€™ data in different brain inter-rhythms. Finally, the Wilcoxon rank sum has been used to test their statistics significant differences. We proposed our understanding and conclusions on this experiment results. Specially, the real-time brain death and coma patientsâ€™ distinguishing is discussed based on parallel computational idea such as cloud computing on Hadoop.
2. 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.
3. Wahlster S, Wijdicks EF, Patel PV, et al. Brain death declaration : Practice and perceptions worldwide[J]. Neurology, 2015, 84: 1870
4. De Georgia MA. History of brain death as death:1968 to the present [J]. Journal of Critical Care, 2014, 29(4):673-678.
5. New York State Department of Health and New York State Task Force on Life &The law. Guidelines for determining brain death,2011.
6. Taylor RM. Reexamining the definition and criteria of death. Semin Neurol 17:265-270.
7. Schneider S. Usefulness of EEG in the evolution of brain death in children: the cons. Electroencephalogram Clinic Neurophysiology, 73(4): 276-278.
8. G.W. Petty, J.P. Mohr, T.A. Pedley, T.K. Tatemichi, L.Lennihan, D.I. Duterte, R.L. Sacco. The role of transcranial Doppler in confirming brain death [J]. Neurology, 1990, 40(2): 300-303.
9. A. Mohandas, Shelley N.Chou. Brian death a clinical and pathological study [J]. Journal of Neurosurgery, 1971, 35(2): 211-218.
10. Kramberger M.G, Kareholt I, Andersson T, Winblad B, Eriksdotter M, Jelic V. Association between EEG abnormalities and CSF biomarkers in a memory clinic cohort [J]. Dementia and Geriatric Cognitive Disorders, 2013, 36(5): 319-328
11. Michael P. Malter, Christina Bahrenberg, Pitt Niehusmann, Christian E. Elger, Rainer Surges. Features of scalp EEG in unilateral mesial temporal lobe epilepsy due to hippocampal sclerosis: Determining factors and predictive value for epilepsy surgery [J]. Clinical Neurophysiology, 2016, 127(2): 1081-1087.
12. Eishi Asano, Carol Pawlak, Aashit Shah, Jagdish Shah, Amiee F. Luat, Judy Ahn-Ewing, Harry T. Chugani. The diagnostic value of initial video-EEG monitoring in children-Review of 1000 cases [J]. Epilepsy Research,2005,66(1):129-135.
13. Chen Weibi, Liu Gang, Jiang Mengdi, Zhang Yan, Liu Yifei, Ye Hong, Fan Linlin, Zhang Yunzhou, Gao Daiquan, Su Yingying. Analysis on the training effect of criteria and practical guidance for determination of brain death: elcectroencephalogram [J]. Chinese Journal of contemporary neurology and neurosurgery, 2015, 15 (12): 965-968.
14. Steven Laureys, Adrian M Owen, Nicholas D Schiff. Review on brain function in coma, vegetative state and related disorders [J]. The LANCET Neurology, 3 (9): 537-546.
15. Eelco F.M. Wijdicks. Views and Reviews: The case against confirmatory tests for determining brain death in adults [J]. Neurology, 75(1): 77-83.
16. Turky Alotaiby, Fathi E Abd EI-Samie, Saleh A Alshebeili, Ishtiaq Ahmad. A review of channel selection algorithms for EEG signal processing [J]. EURASIP Journal on Advances in Signal Processing, 2015(66): 1-21.
17. Fengyu Cong, Qiu-Hua Lin, Lin-Dan Kuang, Xiao-Feng Gong, Piia Astikainen, Tapani Ristaniemi. Tensor decomposition of EEG signals: A brief review [J]. Journal of Neuroscience Methods, 248(6): 59-69.
18. Zhe Chen, Jianting Cao, Yang Cao, Yue Zhang, Fanji Gu, Guoxian Zhu, Zhen Hong, Bin Wang, Andrzej Cichocki. An empirical EEG analysis in brain death diagnosis for adults [J]. Cognitive Neurodynamics, 2008, 2(3): 257-271
19. Jianting Cao, Noboru Murata, Shun-ichi Amari, Andrzej Cichocki, Tsunehiro Takeda. A robust approach to independent component analysis of signals with high-level noise measurement [J]. IEEE transactions on Neural Networks, 2003, 14,631-645
20. Sergio A. Cruces-Alvarez, Andrzej Cichocki, Shun-Ichi Amari. On a new blind signal extraction algorithm: different criteria and stability analysis [J]. IEEE Signal Processing Letters, 2002, 9(8): 233-236.
21. Steven J.Schiff. Akram Aldroubi, Michael Unser, Susumu Sato. Fast wavelet transformation of EEG [J]. Electoencephalography and Clinical Neurophysiology, 1994, 91(6): 442-455
22. Zhang, Y., Liu, B., Ji, X. et al. Neural Process Lett (2016). doi:10.1007/s11063-016-9530-1
23. Bandt, Christoph and Pompe, Bernd. Permutation Entropy: A Natural Complexity Measure for Time Series [J]. Phys. Rev. Lett, 2002, 88(17): 74-102
24. Fan Chun-Ling and Jin Ning-De and Chen Xiu-Ting and Gao Zhong-Ke. Multi-Scale Permutation Entropy: A Complexity Measure for Discriminating Two-Phase Flow Dynamics [J]. Chinese Physics Letters, 2013, 30(9): 5-9
25. PL Nunez, BA Cutilo. Neocortical dynamics and human EEG rhythm [M]. USA: Oxford University, 1995.