Classification of propofol-induced sedation states using brain connectivity analysis

connectivity measurements can provide key information about ongoing brain processes. In this paper, we propose to investigate the performance of the binary classification of Propofol-induced sedation states using partial granger causality analysis. Based on the brain connectivity measurements obtained from EEG signals in a database that contains four sedation states: baseline, mild, moderate, and recovery, we consider eight sensors and evaluate the area under the ROC curve with five classifiers: the k-nearest neighbor (density method), support vector machine, linear discriminant analysis, Bayesian discriminant analysis, and a model based on extreme learning machine. The results support the conclusion that the different Propofol-induced sedation states can be identified with an AUC of around 0.75, by considering signal segments of only 4 second. These results highlight the discriminant power that can be obtained from scalp level connectivity measures for online brain monitoring.