College of Education for Pure Sciences, University of Thi-Qar, Nasiriyah, Iraq.
USQ College, University of Southern Queensland, Toowoomba, QLD, 4350, Australia.
Phys Eng Sci Med. 2022 Sep;45(3):793-808. doi: 10.1007/s13246-022-01145-z. Epub 2022 Jul 5.
Monitoring depth of anaesthesia (DoA) from electroencephalograph (EEG) signals is an ongoing challenge for anaesthesiologists. In this study, we propose an intelligence model that predicts the DoA from a single channel electroencephalograph (EEG) signal. A segmentation technique based on a sliding window is employed to partition EEG signals. Hierarchical dispersion entropy (HDE) is applied to each EEG segment. A set of features is extracted from each EEG segment. The extracted features are investigated using a community graph detection approach (CGDA), and the most relevant features are selected to trace the DoA. The proposed model, based on HDE coupled with CGDA, is evaluated in term of BIS index using several statistical metrics such Q-Q plot, regression, and correlation coefficients. In addition, the proposed model is evaluated against the BIS index in the case of the poor signal quality. The results demonstrated that the proposed model showed an earlier reaction compared with the BIS index when patient's state transits from deep anaesthesia to moderate anaesthesia in the case of poor signal quality. The highest Pearson correlation coefficient obtained by the proposed is 0.96.
监测麻醉深度（DoA）是麻醉师面临的一个持续挑战。在这项研究中，我们提出了一种从单通道脑电图（EEG）信号预测 DoA 的智能模型。使用基于滑动窗口的分割技术对 EEG 信号进行分割。对每个 EEG 段应用分层分散熵（HDE）。从每个 EEG 段提取一组特征。使用社区图检测方法（CGDA）研究提取的特征，并选择最相关的特征来跟踪 DoA。基于 HDE 与 CGDA 相结合的提出的模型使用 Q-Q 图、回归和相关系数等几种统计指标，根据 BIS 指数进行评估。此外，在信号质量较差的情况下，还评估了提出的模型与 BIS 指数的对比。结果表明，在信号质量较差的情况下，当患者的状态从深度麻醉过渡到中度麻醉时，与 BIS 指数相比，提出的模型反应更快。提出的方法获得的最高皮尔逊相关系数为 0.96。
