Entropy and complexity of the electroencephalogram (EEG) have recently been proposed as measures of depth of anesthesia and sedation. Using surrogate data of predefined spectrum and probability distribution we show that the various algorithms used for the calculation of entropy and complexity actually measure different properties of the signal. The tested methods, Shannon entropy (ShEn), spectral entropy, approximate entropy (ApEn), Lempel-Ziv complexity (LZC), and Higuchi fractal dimension (HFD) are then applied to the EEG signal recorded during sedation in the intensive care unit (ICU). It is shown that the applied measures behave in a different manner when compared to clinical depth of sedation score--the Ramsay score. ShEn tends to increase while the other tested measures decrease with deepening sedation. ApEn, LZC, and HFD are highly sensitive to the presence of high-frequency components in the EEG signal.

The ability of two easy-to-calculate nonlinear parameters, the Higuchi fractal dimension (HDf) and spectral entropy, to follow the depth of sedation in the intensive care unit is assessed. For comparison, the relative beta ratio is calculated. The results are evaluated using clinical assessment of the Ramsay score. The results show that the HD/sub f/ discriminates well between Ramsay scores 2-4 while beta ratio is superior for deeper levels of sedation. The value of the HD/sub f/ correlates highly with the cutoff frequency of the low-pass prefilter while spectral entropy is sensitive to the length of the analysis window.

The applicability and performance of spectral entropy as a measure of the depth of sedation was studied by comparison to the Richmond sedation and agitation scale (RASS). A biopotential signal was measured from the forehead of eight ICU patients. From this biopotential four different frequency bands were defined using trend fitting to the low and high frequency limits of the pooled power spectra, two frequency bands representing EEG and the other two representing fEMG. The spectral entropy from the EEG bands correlated very well with the sedation levels of RASS. From levels 0 to -5 the decrease was almost linear (r=0.51 and r=0.53). A similar comparison for the spectral entropy of the fEMG bands did not produce any clear correlation (r=0.07 for both fEMG bands), however there was still some clear interaction at some levels. It seems that the RASS is dependent upon both EEG and fEMG effects. That is; RASS is related to both cortical and sub-cortical components of sedation.