Processed electroencephalography (EEG) monitors such as the bispectral index (BIS) and patient state index (PSI), are used clinically to estimate anesthetic depth, yet their algorithmic design obscures how closely these indices reflect underlying neural complexity. Entropy-based analyses, grounded in information theory, provide a quantitative framework for characterizing EEG signal irregularity and have been proposed as physiologically interpretable alternatives. However, the relationship between these commercial indices and theoretical entropy measures remains unclear. This systematic review aimed to (1) synthesize existing evidence on the relationship between commercially available processed EEG metrics and entropy-based EEG analyses, and (2) identify factors influencing their comparability, including algorithmic, demographic, and anesthetic variables. A comprehensive literature search identified experimental and clinical studies comparing BIS, PSI, and related commercial indices with theoretical entropy measures (e.g., approximate entropy, sample entropy, and permutation entropy, state entropy and response entropy) across various anesthetic agents and clinical populations. Data were extracted on study design, patient demographics, EEG metrics, analytical methods, and reported correlations or prediction probabilities. Ninety-four studies were included, encompassing participants across diverse anesthetic modalities. Overall, BIS exhibited moderate-to-strong correlations with entropy-derived measures and comparable prediction probabilities for distinguishing anesthetic depth. Entropy indices demonstrated greater resistance to certain artifacts but higher susceptibility to electromyographic contamination. Age, anesthetic type, and the use of neuromuscular blocking agents significantly influenced the relationship between indices. Across studies, heterogeneity in preprocessing, entropy algorithms, and patient selection limited direct comparability. Commercially processed EEG indices and theoretical entropy measures capture overlapping but distinct dimensions of cortical dynamics during anesthesia. While both reliably track transitions in consciousness, discrepancies arise from differences in signal filtering, algorithm design, and physiological variability. Future research should prioritize transparent algorithmic frameworks and standardized entropy computation to enhance the interpretability and cross-device comparability of EEG-derived anesthesia monitors.
Vakitbilir et al. (Thu,) studied this question.