AUTHOR=Acker Leah , Ha Christine , Zhou Junhong , Manor Brad , Giattino Charles M. , Roberts Ken , Berger Miles , Wright Mary Cooter , Colon-Emeric Cathleen , Devinney Michael , Au Sandra , Woldorff Marty G. , Lipsitz Lewis A. , Whitson Heather E. TITLE=Electroencephalogram-Based Complexity Measures as Predictors of Post-operative Neurocognitive Dysfunction JOURNAL=Frontiers in Systems Neuroscience VOLUME=15 YEAR=2021 URL=https://www.frontiersin.org/journals/systems-neuroscience/articles/10.3389/fnsys.2021.718769 DOI=10.3389/fnsys.2021.718769 ISSN=1662-5137 ABSTRACT=
Physiologic signals such as the electroencephalogram (EEG) demonstrate irregular behaviors due to the interaction of multiple control processes operating over different time scales. The complexity of this behavior can be quantified using multi-scale entropy (MSE). High physiologic complexity denotes health, and a loss of complexity can predict adverse outcomes. Since postoperative delirium is particularly hard to predict, we investigated whether the complexity of preoperative and intraoperative frontal EEG signals could predict postoperative delirium and its endophenotype, inattention. To calculate MSE, the sample entropy of EEG recordings was computed at different time scales, then plotted against scale; complexity is the total area under the curve. MSE of frontal EEG recordings was computed in 50 patients ≥ age 60 before and during surgery. Average MSE was higher intra-operatively than pre-operatively (