Entropy Measures in Neural Signals
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Entropy measures have been widely used in analyzing neural signals from micro- to macroscales for the normal or abnormal brain assessment. As it is unrealistic to systematic analysis in all the information entropy-based indices in different application areas within limited chapters, we mainly focus on the comparison of the capability of 12 entropy indices derived from electroencephalogram (EEG) for monitoring depth of anesthesia (DoA) and detecting the burst suppression pattern (BSP), in anesthesia induced by GABAergic agents.
Twelve indices were included: response entropy (RE) and state entropy (SE); three wavelet entropy (WE) measures (Shannon WE (SWE), Tsallis WE (TWE), and Renyi WE (RWE)); Hilbert–Huang spectral entropy (HHSE); approximate entropy (ApEn); sample entropy (SampEn); Fuzzy entropy (FuzzyEn); and three permutation entropy (PE) measures (Shannon PE (SPE), Tsallis PE (TPE), and Renyi PE (RPE)). Two EEG data sets recorded from sevoflurane-induced and isoflurane-induced anesthesia, respectively, were selected to assess the capability of each entropy index in DoA monitoring and BSP detection. To validate the effectiveness of these entropy algorithms, pharmacokinetic/pharmacodynamic (PK/PD) modeling and prediction probability (P k ) analysis were applied.
All the entropy indices could track the changes of anesthesia states. Three PE measures outperformed the other entropy indices, with less baseline variability, higher coefficient of determination (R2) and prediction probability, and RPE performed best; while ApEn and SampEn discriminated BSP best.
Each entropy index has its advantages and disadvantages in estimating DoA. Overall, it is suggested that the RPE index has a superior performance. Investigating the advantages and disadvantages of these entropy indices could help improve current clinical indices for monitoring DoA.
KeywordsEEG Anesthesia Entropy Pharmacokinetic/Pharmacodynamic modeling Depth of anesthesia
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