AI 中文总结
研究旨在寻找替代脑电双频指数（BIS）监测麻醉深度的指标，提出用置换熵（PeEn）和差分熵（EoD）两种基于熵的脑电图测量方法，通过与PK/PD模型生成的模拟BIS值比较评估性能，结果显示二者性能与模拟BIS相当，或可有效监测麻醉深度。
AI 中文摘要
麻醉深度是手术或其他操作中要分析的复杂但重要的生命状态。脑电双频指数（BIS）是评估该状态的一个参数，基于脑电图，范围0到100，手术稳定状态目标值40到60。因其算法未公开，促使寻找替代指标。本文研究置换熵（PeEn）和差分熵（EoD）两种基于熵的脑电图测量方法替代BIS用于麻醉监测。通过与Marsh、Schnider和Eleveld提出的药代动力学/药效学（PK/PD）模型生成的模拟BIS值比较评估其性能，计算各模型模拟BIS与记录的BIS、PeEn或EoD之间的均方根误差（RMSE）。Wilcoxon秩和检验统计分析表明，在研究的PK/PD模型中，模拟BIS、PeEn和EoD的中位数RMSE值无显著差异。结果表明PeEn和EoD性能与模拟BIS相当，可能是监测麻醉深度有前景的基于脑电图的指标。
英文摘要
Depth of anesthesia is a complex but important vital state to analyze during a surgery or other procedure. One parameter to estimate this state is the bispectral index (BIS), a value ranging from 0 to 100 with a target of 40 to 60 for a stable state during surgery, which is based on the electroencephalogram (EEG). Despite its widespread clinical use, the BIS remains the subject of ongoing discussion as the exact algorithm underlying the BIS is not publicly disclosed, motivating the search for alternative EEG-based indices. In this publication, two entropy-based EEG measures, Permutation Entropy (PeEn) and Entropy of Difference (EoD) are investigated as potential alternatives to replace the BIS for anesthesia monitoring. Both measures quantify the complexity and irregularity of EEG signals and have previously been proposed as indicators of changes in consciousness and anesthetic depth. Their performance is evaluated by comparing with the simulated BIS values generated using the pharmacokinetic/pharmacodynamic (PK/PD) models, proposed by Marsh, Schnider and Eleveld. For each model, the root mean squared error (RMSE) between the simulated BIS and the recorded BIS, PeEn, or EoD is calculated, respectively. Statistical analysis using the Wilcoxon rank-sum test reveal no significant differences between the median RMSE values of the simulated BIS, PeEn, and EoD across the investigated PK/PD models. These results suggest that PeEn and EoD provide performance comparable to that of the simulated BIS, indicating that they may represent promising EEG-based indicators for monitoring depth of anesthesia.
Comments9 pages, 4 figures, submitted to the 12th Federation of European Simulation Societies Conference (Eurosim 2026)
