Estimation of the depth of anesthesia by using a multioutput least-square support vector regression

Abstract

Today, most surgeries are performed under general anesthesia where one of the most growing methods foranesthesia depth monitoring is using electroencephalogram (EEG). The bispectral index (BIS) is the most commonlyused parameter for anesthesia depth monitoring using EEG, the validity of which is still to be studied before beingaccepted as a routine method by clinicians. This paper proposes a new technique for detecting the depth of anesthesiaby means of EEG, which is based on multioutput least-squares support vector regression (MLS-SVR), which provides theprobability that the patient is in the four different possible anesthesia states. In this study, EEG signals were recordedfrom 20 patients who were anesthetized in the operation room. Twelve linear and nonlinear EEG features were thenextracted every 10 s from the EEG signals to form the feature vector. The features were then classified by the MLS-SVRclassifier and the results were compared with those of the BIS, where no significant differences were observed (P > 0.05).Due to using the MLS-SVR classifier, which replaces quadratic equations by linear equations, the proposed method showsa higher accuracy compared to the other previously reported methods