Assessing EEG slow wave activity during anesthesia using Hilbert-Huang Transform

Slow waves (<; 1 Hz) are considered to be the most important electroencephalogram (EEG) signature of non-rapid eye movement sleep and have substantial physiological importance. In addition to natural sleep, slow waves can be seen in the EEG during general anesthesia offering great potential for depth of anesthesia monitoring. In this paper, Hilbert-Huang Transform, an adaptive data-driven method designed for the analysis on non-stationary data, was used to investigate the dynamical changes in the EEG slow wave activity during induction of anesthesia with propofol. The method was found to be able to extract stable signal components representing slow wave activity that were consistent between patients. The signal analysis revealed a possible specific structure between different components dependent on the depth of anesthesia on which further studies are needed.