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.
Wang Yong,Liang Zhenhu,Xia Xiaoyu, et al. Clinical Application of Resting EEG in Consciousness Diagnosis[J]. Chinese Journal of Biomedical Engineering, 2021, 40(2): 154-162.
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