OBJECTIVE Constructing a framework to evaluate consciousness is an important issue in neuroscience research and clinical practice. However, there is still no systematic framework for quantifying altered consciousness along the dimensions of both level and content. This study builds a framework to differentiate the following states: coma, general anesthesia, minimally conscious state (MCS), and normal wakefulness. METHODS This study analyzed electroencephalography (EEG) recorded from frontal channels in patients with disorders of consciousness (either coma or MCS), patients under general anesthesia, and healthy participants in normal waking consciousness (NWC). Four non-linear methods-permutation entropy (PE), sample entropy (SampEn), permutation Lempel-Ziv complexity (PLZC), and detrended fluctuation analysis (DFA)-as well as relative power (RP), extracted features from the EEG recordings. A genetic algorithm-based support vector machine (GA-SVM) classified the states of consciousness based on the extracted features. A multivariable linear regression model then built EEG indices for level and content of consciousness. RESULTS The PE differentiated all four states of consciousness (p<0.001). Altered contents of consciousness for NWC, MCS, coma, and general anesthesia were best differentiated by the SampEn, and PLZC. In contrast, the levels of consciousness for these four states were best differentiated by RP of Gamma and PE. A multi-dimensional index, combined with the GA-SVM, showed that the integration of PE, PLZC, SampEn, and DFA had the highest classification accuracy (92.3%). The GA-SVM was better than random forest and neural networks at differentiating these four states. The 'coordinate value' in the dimensions of level and content were constructed by the multivariable linear regression model and the non-linear measures PE, PLZC, SampEn, and DFA. CONCLUSIONS Multi-dimensional measurements, especially the PE, SampEn, PLZC, and DFA, when combined with GA-SVM, are promising methods for constructing a framework to quantify consciousness.

中文翻译：

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基于非线性测量和基于遗传算法的支持向量机相结合的意识表的构建。

目的建立评估意识的框架是神经科学研究和临床实践中的重要问题。然而，仍然没有一个系统的框架来量化水平和内容维度上改变的意识。这项研究建立了一个区分以下状态的框架：昏迷，全身麻醉，最低意识状态（MCS）和正常觉醒。方法：本研究分析了意识障碍患者（昏迷或MCS），全身麻醉患者和正常醒觉（NWC）健康参与者从额叶通道记录的脑电图（EEG）。四种非线性方法-置换熵（PE），样本熵（SampEn），置换Lempel-Ziv复杂度（PLZC）和去趋势波动分析（DFA）-以及相对功率（RP），从脑电图记录中提取特征。基于遗传算法的支持向量机（GA-SVM）根据提取的特征对意识状态进行分类。然后，多元线性回归模型建立了意识水平和内容的EEG指数。结果PE区分了所有四种意识状态（p <0.001）。Namp，MCS，昏迷和全身麻醉的意识改变内容最好通过SampEn和PLZC加以区分。相比之下，这四种状态的意识水平最好通过γ和PE的RP来区分。多维索引与GA-SVM结合使用，表明PE，PLZC，SampEn和DFA的集成具有最高的分类准确性（92.3％）。在区分这四个状态方面，GA-SVM优于随机森林和神经网络。通过多变量线性回归模型和非线性度量PE，PLZC，SampEn和DFA构造水平和含量维度中的“坐标值”。结论多维测量，尤其是PE，SampEn，PLZC和DFA，与GA-SVM结合使用时，是构建意识量化框架的有前途的方法。