title: 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 feature detection in electroencephalogram (EEG) signals. Several spectral parameters derived from EEG have been proposed for measuring DOA in clinical applications. In the 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 anesthetic drug effects. Fifty-six patients undergoing general anesthesia in an operating theatre 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 the improved method can clearly discriminate the conscious state from the anesthetized state after filtration (p  <  0.05). A significantly larger mean AUC (p  <  0.05) shows that the improved method performs better than the conventional method to measure the DOA in most circumstances. Especially for raw EEG contaminated by artifacts, 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 are more useful to measure the DOA in noisy cases.
authors.affiliation: Key Laboratory of Fiber Optic Sensing Technology and Information Processing, Ministry of Education, Wuhan University of Technology, Wuhan, Hubei 430070 People's Republic of China. School of Information Engineering, Wuhan University of Technology, Wuhan, Hubei 430070, People's Republic of China.
authors.forename: Quan
authors.initials: Q
authors.lastname: Liu
authors.forename: Yi-Feng
authors.initials: YF
authors.lastname: Chen
authors.forename: Shou-Zen
authors.initials: SZ
authors.lastname: Fan
authors.forename: Maysam F
authors.initials: MF
authors.lastname: Abbod
authors.forename: Jiann-Shing
authors.initials: JS
authors.lastname: Shieh
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000768:Anesthesia, General; D016477:Artifacts; D004569:Electroencephalography; D006801:Humans; D008875:Middle Aged; D012372:ROC Curve; D012815:Signal Processing, Computer-Assisted; D059629:Signal-To-Noise Ratio; D055815:Young Adult