Quantifying cortical activity during general anesthesia using wavelet analysis

This paper reports on a novel method for quantifying the cortical activity of a patient during general anesthesia as a surrogate measure of the patient´s level of consciousness. The proposed technique is based on the analysis of a single-channel (frontal) electroencephalogram (EEG) signal using stationary wavelet transform (SWT). The wavelet coefficients calculated from the EEG are pooled into a statistical representation, which is then compared to two well-defined states: the awake state with normal EEG activity, and the isoelectric state with maximal cortical depression. The resulting index, referred to as the wavelet-based anesthetic value for central nervous system monitoring (WAV_CNS), quantifies the depth of consciousness between these two extremes. To validate the proposed technique, we present a clinical study which explores the advantages of the WAV_CNS in comparison with the BIS monitor (Aspect Medical Systems, MA), currently a reference in consciousness monitoring. Results show that the WAV_CNS and BIS are well correlated (r=0.969) during periods of steady-state despite fundamental algorithmic differences. However, in terms of dynamic behavior, the WAV_CNS offers faster tracking of transitory changes at induction and emergence, with an average lead of 15-30 s. Furthermore, and conversely to the BIS, the WAV_CNS regains its preinduction baseline value when patients are responding to verbal command after emergence from anesthesia. We conclude that the proposed analysis technique is an attractive alternative to BIS monitoring. In addition, we show that the WAV_CNS dynamics can be modeled as a linear time invariant transfer function. This index is, therefore, well suited for use as a feedback sensor in advisory systems, closed-loop control schemes, and for the identification of the pharmacodynamic models of anesthetic drugs.