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- W2898919618 abstract "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." @default.
- W2898919618 created "2018-11-09" @default.
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- W2898919618 date "2018-07-01" @default.
- W2898919618 modified "2023-09-23" @default.
- W2898919618 title "Classification of propofol-induced sedation states using brain connectivity analysis" @default.
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- W2898919618 doi "https://doi.org/10.1109/embc.2018.8512275" @default.
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