Clinical Application of Resting EEG in Consciousness Diagnosis
Wang Yong1, Liang Zhenhu1, Xia Xiaoyu2, Bai Yang3, Yang Yi2, Liu Yangfeng4, He Jianghong2, Li Xiaoli5*
1(Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Yanshan University, Qinhuangdao 066004, Hebei, China) 2(Department of Neurosurgery, The Seventh Medical Center, General Hospital of Chinese PLA, Beijing 100700, China) 3(Department of Basic Medicine, School of Medicine, Hangzhou Normal University, Hangzhou 311121, China) 4(Department of Neurology, Airforce 986 Hospital, Xi'an 710000, China) 5(State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China)
Abstract：The accurate diagnosis of patients with disorder of consciousness (DOC) is of great significance for the treatment plan and outcome, so it is very necessary to develop a reliable method to assess the level of consciousness. In this work, fifty patients diagnosed with DOC (25 vegetable state (VS) patients and 25 minimally consciousness state (MCS) patients) were enrolled. The EEG data were obtained through Bispectral index (BIS) monitor; information complexity and relative power were calculated. The differences of EEG characteristics between MCS and VS groups were analyzed by two sample t-test, and the quantitative relationship between EEG characteristics and clinical scores was analyzed by Pearson correlation analysis. Exploring EEG characteristics to distinguish the states of consciousness. The EEG characteristics were used to build machine-learning model and explore its potential in clinical diagnosis. The results showed that permutation entropy (PE), permutation Lempel-Ziv complexity (PLZC) and the relative power of gamma band were able to distinguish different states of consciousness (PE: 0.71±0.07, 0.75±0.07, P<0.01; PLZC: 0.53±0.07, 0.56±0.06, P<0.01; gamma: 0.13±0.07, 0.16±0.06, P<0.01). PE shows the highest correlation (r=0.81, P<0.05). The area under the ROC curve (AUC) and accuracy (ACC) of consciousness classification model based on PE (AUC=0.931, ACC=0.92) was better than that of BIS (AUC=0.905, ACC=0.90). In conclusion, the resting EEG can be used as an important method for the diagnosis of consciousness.
王勇, 梁振虎, 夏小雨, 白洋, 杨艺, 刘养凤, 何江弘, 李小俚. 静息态脑电在意识诊断中的临床应用[J]. 中国生物医学工程学报, 2021, 40(2): 154-162.
Wang Yong, Liang Zhenhu, Xia Xiaoyu, Bai Yang, Yang Yi, Liu Yangfeng, He Jianghong, Li Xiaoli. Clinical Application of Resting EEG in Consciousness Diagnosis. Chinese Journal of Biomedical Engineering, 2021, 40(2): 154-162.
 Bernat JL. Chronic disorders of consciousness [J]. Lancet, 2006, 367(9517): 1181-1192.  Laureys S, Owen A M, Schiff N D. Brain function in coma, vegetative state, and related disorders [J]. Lancet Neurology, 2004, 3(9): 537-546.  Giacino J, Kalmar K, Whyte J. The JFK Coma Recovery Scale-Revised: Measurement characteristics and diagnostic utility [J]. Arch Phys Med Rehabil, 2004, 85(12): 2020-2029.  Teasdale G, Jennett B. Assessment of coma and impaired consciousness. A practical scale [J]. Lancet, 1974, 2(7872): 81.  Bai Y, Xia X, Li X. A review of resting-state electroencephalography analysis in disorders of consciousness [J]. Frontiers in Neurology, 2017, 8:471.  Yujin Z, Yi Y, Juanning S, et al. Influence of inter-stimulus interval of spinal cord stimulation in patients with disorder of consciousness [J]. Neuroimage Clin, 2017, 17: 1-9.  Cruse D, Monti MM, Owen AM. Neuroimaging in disorders of consciousness: Contributions to diagnosis and prognosis [J]. Future Neurology, 2011, 6(2): 291-299.  Ellerkmann RK, Soehle MG, Zinserling J, et al. The entropy module and bispectral index as guidance for propofol-remifentanil anaesthesia in combination with regional anaesthesia compared with a standard clinical practice group [J]. Anaesthesia & Intensive Care, 2010, 38(1): 159.  Liang Z, Duan L, Li X. Comparison of permutation entropy index and bispectral index for monitoring effects of anesthetic drugs to brain activity[C]//Proceedings of the Proceedings of the 2nd International Conference on BioMedical Engineering and Informatics. Tianjin: IEEE, 2009:10956383.  Engemann DA, Raimondo F, King JR, et al. Robust EEG-based cross-site and cross-protocol classification of states of consciousness [J]. Brain: A Journal of Neurology, 2018, 141(11): 3179-3192.  Christoph B, Bernd P. Permutation entropy: A natural complexity measure for time series [J]. Physical Review Letters, 2002, 88(17): 174102.  Zhenhu L, Yinghua W, Xue S, et al. EEG entropy measures in anesthesia [J]. Front Comput Neurosci, 2015, 9: 9-16.  Bai Y, Liang Z, Li X, et al. Permutation Lempel-Ziv complexity measure of electroencephalogram in GABAergic anaesthetics [J]. Physiological Measurement, 2015, 36(12): 2483-2501.  Muhammad A. Razi KA. A comparative predictive analysis of neural networks (NNs), nonlinear regression and classification and regression tree (CART) models [J]. Expert Systems with Applications, 2005, 29(1): 65-74.  Martha E, Nunn JF, Su Xiaogang, et al. Development of prognostic indicators using classification and regression trees for survival [J]. Periodontology 2000, 2012, 58(1): 134-142.  Goldfine AM, Victor JD, Conte MM, et al. Bedside detection of awareness in the vegetative state [J]. Lancet, 2012, 381(9863): 1701-1702.  Cruse D, Beukema S, Chennu S, et al. The reliability of the N400 in single subjects: Implications for patients with disorders of consciousness [J]. Neuroimage Clinical, 2014, 4: 788-799.  Jacobo Diego S, Jean-Remi K, Imen EK, et al. Large scale screening of neural signatures of consciousness in patients in a vegetative or minimally conscious state [J]. Brain, 2014,138(Pt 8):2258-2270.  Piarulli A, Bergamasco M, Thibaut A, et al. EEG ultradian rhythmicity differences in disorders of consciousness during wakefulness [J]. Journal of Neurology, 2016, 263(9): 1-15.  Mahon P, Greene B, EM, Mcnamara B, et al. Can state or response entropy be used as a measure of sleep depth? [J]. Anaesthesia, 2010, 63(12): 1309-1313.  Vanluchene AL. Spectral entropy as an electroencephalographic measure of anesthetic drug effect: a comparison with bispectral index and processed midlatency auditory evoked response [J]. Anesthesiology, 2004, 101(1): 34-42.  Hansotia PL. Persistent vegetative state. Review and report of electrodiagnostic studies in eight cases [J]. Archives of Neurology, 1985, 42(11): 1048.  Başar E, Bullock TH. Induced Rhythms in the Brain [M]. Beilin: Springer,1992.  Victor JD, Drover JD, Conte MM, et al. Mean-field modeling of thalamocortical dynamics and a model-driven approach to EEG analysis [J]. Proc Natl Acad Sci U S A, 2011, 108(3): 15631-15638.  Laureys S, Berré J, Elincx S, et al. Differences in brain metabolism between patients in vegetative state, minimally conscious state and ‘locked in syndrome’ [J]. Neuroimage, 2003, 13(6): 806.  Diego SJ, Jean-Remi K, Imen EK, et al. Large scale screening of neural signatures of consciousness in patients in a vegetative or minimally conscious state [J]. Brain, 2014, 137(Pt 8):2258-2270.  Thul A, Lechinger J, Donis J, et al. EEG entropy measures indicate decrease of cortical information processing in disorders of consciousness [J]. Clinical Neurophysiology, 2016, 127(2): 1419-1427.  Li D, Li X, Liang Z, et al. Multiscale permutation entropy analysis of EEG recordings during sevoflurane anesthesia [J]. J Neural Eng, 2010, 7(4): 046010.  Bruhn J, Röpcke H, Hoeft A. Approximate entropy as an electroencephalographic measure of anesthetic drug effect during desflurane anesthesia [J]. Anesthesiology, 2000, 92(3): 715-726.  Chennu S, Annen J, Wannez S, et al. Brain networks predict metabolism, diagnosis and prognosis at the bedside in disorders of consciousness [J]. Brain, 2017, 140(8):2120-2132.  Schnakers C, Ledoux D, Majerus S, et al. Diagnostic and prognostic use of bispectral index in coma, vegetative state and related disorders [J]. Brain Inj, 2008, 22(12): 926-931.