The depth of anesthesia monitoring is helpful to guide administrations of general anesthetics during surgical procedures,however, the conventional 2-4 channels electroencephalogram (EEG) derived monitors have their limitations in monitoring conscious states due to low spatial resolution and suboptimal algorithm. In this study, 256-channel high-density EEG signals in 24 subjects receiving three types of general anesthetics (propofol, sevoflurane and ketamine) respectively were recorded both before and after anesthesia. The raw EEG signals were preprocessed by EEGLAB 14.0. Functional connectivity (FC) analysis by traditional coherence analysis (CA) method and a novel sparse representation (SR) method. And the network parameters, average clustering coefficient (ACC) and average shortest path length (ASPL) before and after anesthesia were calculated. The results show SR method find more significant FC differences in frontal and occipital cortices, and whole brain network (p<0.05). In contrast, CA can hardly obtain consistent ASPL in the whole brain network (p>0.05). Further, ASPL calculated by SR for whole brain connections in all of three anesthesia groups increased, which can be a unified EEG biomarker of general anesthetics-induced loss of consciousness (LOC). Therefore FC analysis based on SR analysis has better performance in distinguishing anesthetic-induced LOC from awake state.

中文翻译：

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全身麻醉期间的静息状态功能连接分析：一项高密度脑电图研究

麻醉深度监测有助于指导手术过程中的全身麻醉给药，但传统的 2-4 通道脑电图 (EEG) 衍生监测器由于空间分辨率低和算法欠佳，在监测意识状态方面存在局限性。在这项研究中，在麻醉前后分别记录了 24 名接受三种全身麻醉剂（丙泊酚、七氟醚和氯胺酮）的受试者的 256 通道高密度脑电信号。原始 EEG 信号由 EEGLAB 14.0 预处理。通过传统的相干分析 (CA) 方法和新颖的稀疏表示 (SR) 方法进行功能连通性 (FC) 分析。并计算麻醉前后的网络参数、平均聚类系数（ACC）和平均最短路径长度（ASPL）。结果表明，SR方法发现额叶和枕叶皮质以及全脑网络的FC差异更显着（p<0.05）。相比之下，CA 很难在全脑网络中获得一致的 ASPL（p>0.05）。此外，由 SR 计算的所有三个麻醉组中全脑连接的 ASPL 都增加了，这可以作为全身麻醉药引起的意识丧失 (LOC) 的统一 EEG 生物标志物。因此，基于 SR 分析的 FC 分析在区分麻醉诱导的 LOC 和清醒状态方面具有更好的性能。由 SR 计算的所有三个麻醉组全脑连接的 ASPL 均增加，这可能是全身麻醉药引起的意识丧失 (LOC) 的统一 EEG 生物标志物。因此，基于 SR 分析的 FC 分析在区分麻醉诱导的 LOC 和清醒状态方面具有更好的性能。由 SR 计算的所有三个麻醉组全脑连接的 ASPL 均增加，这可能是全身麻醉药引起的意识丧失 (LOC) 的统一 EEG 生物标志物。因此，基于 SR 分析的 FC 分析在区分麻醉诱导的 LOC 和清醒状态方面具有更好的性能。