A wavelet-based estimating depth of anesthesia

In this paper, an efficient method for quantifying the depth of anesthesia using underlying content of electroencephalogram (EEG) signal is presented. This method could be as an alternative instead of other clinical criteria such as pain reflex, auditory evoked potential, bispectral index scale (BIS) or amount of burst suppression. The proposed method is based on analysis of a single-channel EEG signal of patients during anesthesia, using wavelet transform. In order to use wavelet information, entropy is selected as the statistical tool. The obtained results suggest our method called Wavelet Coefficient Energy Entropy (WCEE) as a quantitative index for depth of anesthesia. To validate the introduced index, WCEE is applied to EEG signals of 22 people during the surgery and their determined indices are compared to BIS index, which is now a reference in anesthesia monitoring. The comparison results reveal a high correlation between WCEE index and BIS index during different anesthesia states. Moreover, WCEE values could precisely classify different anesthesia states with less computational burden than BIS index.