Higuchi fractal dimension and spectral entropy as measures of depth of sedation in intensive care unit.

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.

Application of spectral entropy to EEG and facial EMG frequency bands for the assessment of level of sedation in ICU.

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.

Comparison of entropy and complexity measures for the assessment of depth of sedation.

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).