An intelligence Model Based on Brain Signals for Deep anesthesia and Awake stages classification
Keywords:
Electroencephalogram, Deep Anesthesia, Depth of Anesthesia (DoA), Hierarchical Dispersion Entropy (HDE),, Classification,Abstract
Monitoring The depth of anesthesia (DoA) is One of the current challenges in medicine. An accurate DoA
can deliver an adequate amount of anastatic medications that could reduce the risk of consciousness or
excessive anesthesia. In this study, an intelligent model classifies deep and awake stages from an
electroencephalogram (EEG) signal was used. It consists of two stages that are considered vital in
designing the DoA. A hierarchical dispersion entropy (HDE) was applied to de-noised EEG segments. In
this study, the EEG signal is decomposed into four experimentally levels. Then DE entropy features are
extracted from each band. An LS-SVM classifier is used to classify the extracted features into two
anesthetic states. We then compared the results of the Least Squares Support Vector Machines LS-SVM
classifier with other types of classifiers such as Multi-class-SVM and k-nearest. We found that the results
of our study were the best because the accuracy reached by us was (95.23%) higher than the other two
classifiers.
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