严重意识障碍患者康复过程EEG非线性特征与CRSR评分相关性分析与可视化表征

doi:10.3969/j.issn.0258-8021. 2015.02.004

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Abstract To provide an electrophysiology judgment method for evaluating the rehabilitation process of severe disorders of consciousness (DOC) patients, the correlation between EEG nonlinear characteristics and CRSR score was studied, and a visual characterization method was developed. The EEG data (acquired twice in a quiet state) of awareness patient’s (minimally conscious state, MCS, 10 and vegetative state, VS, 8) rehabilitation process were collected, nonlinear characteristics (approximate entropy, sample entropy, permutation entropy and complexity LZC) were calculated to compare the changes between nonlinear characteristics and CRSR score, and then were used to brain mapping for visual characterization. The CRSR score changes significantly both for MCS and VS patients (P<001). Approximate entropy and complexity LZC changes significantly for MCS patients (P<005), while only approximate entropy changes significantly for VS patients (P<005). There has significantly positive correlation of score changes between CRSR and nonlinear characteristics (approximate entropy for both MCS and VS, complexity LZC for MCS only), the correlation coefficients were calculated, 0851 and 0693 for MCS (approximate entropy and complexity LZC), and 0778 for VS (approximate entropy). Approximate entropy can be used to visualize patient’s brain function mapping. Approximate entropy could be used as a potential method to evaluate the rehabilitation process for patients with awareness.

Key words： severe disorders of consciousness rehabilitation awareness EEG nonlinear characteristics CRSR brain electrical activity mapping

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	Hao Xueliang¹Yang Wenwei¹Li Jingqi²Liu Xiaoping^1

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Hao Xueliang¹Yang Wenwei¹Li Jingqi²Liu Xiaoping^1. Correlation Analysis of Nonlinear Characteristics in EEG with CRSR Score and Visual Characterization of Rehabilitation Process in DOC Patients[J]. journal1, 2015, 34(2): 153-159.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021. 2015.02.004 OR http://cjbme.csbme.org/EN/Y2015/V34/I2/153

［1］Giacino JT, Ashwal S, Childs N, et al. The minimally conscious state: definition and diagnostic criteria ［J］. Neurology, 2002, 58: 349-353.
［2］Kotchoubey B, Lang S, Mezger G, et al. Information processing in severe disorders of consciousness: Vegetative state and minimally conscious state［J］. Clinical Neurophysiology, 2005, 116:2441-2453.
［3］Giacino JT, Kalmar K, Whyte J. The JFK Coma Recovery ScaleRevised: measurement characteristics and diagnostic utility ［J］. Archives of Physical Medicine and Rehabilitation, 2004, 85(12): 2020-2029.
［4］Schnakers C, Majerus S, Giacino J, et al. A French validation study of the Coma Recovery ScaleRevised (CRSR)［J］. Brain Injury, 2008, 22(10): 786-792.
［5］Eken C, Kartal M, Bacanli A, et al. Comparison of the Full Outline of Unresponsiveness Score Coma Scale and the Glasgow Coma Scale in an emergency setting population ［J］. European Journal of Emergency Medicine, 2009, 16(1): 29-36.
［6］Gosseries O, Schnakers C, Ledoux D, et al. Automated EEG entropy measurements in coma,vegetative state/unresponsive wakefulness syndrome and minimally conscious state［J］. Functional Neurology, 2011, 26(1): 25-30.
［7 ］Cruse D, Chennu S, Chatelle C, et al. Bedside detection of awareness in the vegetative state: a cohort study［J］. Lacent, 2011, 378(9809): 2088-2094.［8］Schnakers C, Ledoux D, Majerus S, et al. Diagnostic and prognostic use of bispectral index in coma, vegetative state, and related disorders ［J］, Brain Injury, 22(2008): 926-931.
［9］Cruse D, Chennu S, Chatelle C, et al. Bedside detection of awareness in the vegetative state: a cohort study ［J］. The Lancet, 2011, 378(9809): 2088-2094.
［10］Schnakers C, Perrin F, Schabus M, et al. Voluntary brain processing in disorders of consciousness ［J］. Neurology 2008, 71(20): 1614-1620.
［11］Gosseries O, Schnakers C, Ledoux D, et al. Automated EEG entropy measurements in coma, vegetative state/unresponsive wakefulness syndrome and minimally conscious state ［J］. Functional Neurology, 2011, 26(1): 1-6.
［12］尧德中. 脑功能探测的电学理论与方法［M］. 北京:科学出版社,2003.
［13］刘名顺. 脑地形图及其临床应用［J］. 现代电生理学杂志, 2008, 15(3): 173-187.
［14］黎臧, 邱志诚, 顾凡及. 复杂度脑电地形图研究［J］. 生物物理学报, 2000, 16(1): 114-118.
［15］Pincus M, Singer BH. Randomness and degree of irregularity ［J］. Proc Natl Acad Sci USA, 1996, 93: 2083-2088.
［16］Richman JS, Moorman JR. Physiological timeseries analysis using approximate entropy and sample entropy ［J］. Am J Physiol Heart Circ Physiol, 2000, 278: 2039-2049.
［17］Bandt C, Pompe B. Permutation entropy: A natural complexity measure for time series ［J］. Phys Rev Lett, 2002, 88(17): 1-4.
［18］Lempel A, Ziv J. On the complexity of finite sequence ［J］. IEEE Transactions on Information Theory, 1976, 22(1): 75-81.
［19］Delorme A, Makeig S. EEGLAB: an open source toolbox for analysis of singletrial EEG dynamics including independent component analysis［J］. Neuroscience Methods, 2004, 134(1): 9-21.