Improved spectrum analysis in EEG for measure of depth of anesthesia based on phase-rectified signal averaging

Abstract

The definition of the depth of anesthesia (DOA) is still controversial and its measurement is not completely standardized in modern anesthesia. Power spectral analysis is an important method for features detection in electroencephalogram (EEG) signals. Several spectral parameters derived from EEG have been proposed to measuring depth of anesthesia (DOA) for clinical application. In present paper, an improved method based on phase-rectified signal averaging (PRSA) is designed to improve the predictive accuracy of relative alpha and beta power, a frequency band power ratio, total power, median frequency (MF), spectral edge frequency 95 (SEF95), and spectral entropy for assessing the anesthetic drug effects. Fifty six patients undergoing general anesthesia in operation room are studied. All EEG signals are continuously recorded from the awake state to the end of the recovery state and then filtered using multivariate empirical mode decomposition (MEMD). All parameters are evaluated using the commercial bispectral index (BIS) and expert assessment of conscious level (EACL), respectively. The ability to predict DOA is estimated using the area under the receiver-operator characteristics curve (AUC). All indicators based on improved method can clearly discriminate the conscious state from anesthetized state after filtration (p<0.05). A significantly larger mean AUC (p<0.05) shows that the improved method accurately performs better than the conventional method to measure the DOA in most circumstances. Especially for raw EEG contaminated by artefacts, when the BIS index is used to indicate the consciousness level, the improvement is 7.37% (p<0.05), 9.04% (p<0.05), 18.46% (p<0.05), 27.73% (p<0.05), 14.65% (p<0.05), 2.52%, 5.38% and 6.24% (p<0.05) for relative alpha and beta power, power ratio, total power, MF, SEF, RE and SE, respectively. However, when the EACL is used to indicate the consciousness level, the improvement is 3.30% (p<0.05), 16.69% (p<0.05), 15.08% (p<0.05), 34.83% (p<0.05), 27.78% (p<0.05), 5.89% (p<0.05), 26.05% (p<0.05) and 23.42% (p<0.05). Spectral parameters derived from PRSA perform more useful to measure the DOA under noisy cases.