Abstract：
Anaesthesia, crucial to surgical practice, is undergoing renewed scrutiny due to the integration of artificial intelligence in its medical use. The precise control over the temporary loss of consciousness is vital to ensure safe, pain-free procedures. Traditional methods of depth of anaesthesia (DoA) assessment, reliant on physical characteristics, have proven inconsistent due to individual variations. In response, electroencephalography (EEG) techniques have emerged, with indices such as the Bispectral Index offering quantifiable assessments. This literature review explores the current scope and frontier of DoA research, emphasising methods utilising EEG signals for effective clinical monitoring. This review offers a critical synthesis of recent advances, specifically focusing on electroencephalography (EEG) techniques and their role in enhancing clinical monitoring. By examining 117 high-impact papers, the review delves into the nuances of feature extraction, model building, and algorithm design in EEG-based DoA analysis. Comparative assessments of these studies highlight their methodological approaches and performance, including clinical correlations with established indices like the Bispectral Index. The review identifies knowledge gaps, particularly the need for improved collaboration for data access, which is essential for developing superior machine learning models and real-time predictive algorithms for patient management. It also calls for refined model evaluation processes to ensure robustness across diverse patient demographics and anaesthetic agents. The review underscores the potential of technological advancements to enhance precision, safety, and patient outcomes in anaesthesia, paving the way for a new standard in anaesthetic care. The findings of this review contribute to the ongoing discourse on the application of EEG in anaesthesia, providing insights into the potential for technological advancement in this critical area of medical practice.
摘要：
麻醉,对外科手术至关重要，由于人工智能在其医疗用途中的整合，正在接受新的审查。对暂时失去意识的精确控制对于确保安全至关重要，无痛的程序。传统的麻醉深度（DoA）评估方法，依赖于物理特征，已经证明由于个体差异而不一致。作为回应,脑电图（EEG）技术已经出现，双频指数等指数提供可量化的评估。这篇文献综述探讨了DoA研究的当前范围和前沿，强调利用脑电信号进行有效临床监测的方法。这篇综述提供了对最新进展的关键综合，特别关注脑电图（EEG）技术及其在加强临床监测中的作用。通过研究117份高影响力论文，这篇评论深入研究了特征提取的细微差别，模型建筑,以及基于EEG的DoA分析中的算法设计。对这些研究的比较评估突出了它们的方法论方法和性能，包括与双频指数等既定指标的临床相关性。审查确定了知识差距，特别是需要改进数据访问的协作，这对于开发卓越的机器学习模型和用于患者管理的实时预测算法至关重要。它还要求完善的模型评估过程，以确保跨不同患者人口统计学和麻醉剂的鲁棒性。审查强调了技术进步提高精确度的潜力，安全,和病人在麻醉中的结果，为麻醉护理的新标准铺平了道路。这篇综述的发现有助于关于脑电图在麻醉中的应用的持续讨论，提供对医学实践这一关键领域技术进步潜力的见解。
