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.
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