Electroencephalogram spindle patterns corresponding to two different phenomena-natural sleep and propofol anesthesia-are compared. The spindles are extracted from 5 overnight sleep recordings and 10 recordings of deep propofol anesthesia. Mean frequency, angle of the trend in instant frequency as well as 3 nonlinear parameters-spectral entropy, approximate entropy, and Higuchi fractal dimension- are calculated to characterize the spindle waveforms. Using the Wilcoxon rank sum test with significance level of 0.01, all the mentioned features, except approximate entropy, differ significantly for the two types of EEG spindles.
In this paper 5 methods for the assessment of signal entropy are compared in their capability to follow the changes in the EEG signal during transition from continuous EEG to burst suppression in deep anesthesia. To study the sensitivity of the measures to phase information in the signal, phase randomization as well as amplitude adjusted surrogates are also analyzed. We show that the selection of algorithm parameters and the use of normalization are important issues in interpretation and comparison of the results. We also show that permutation entropy is the most sensitive to phase information among the studied measures and that the EEG signal during high amplitude delta activity in deep anesthesia is of highly nonlinear nature.
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