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

Today, most surgeries are performed under general anesthesia where one of the most growing methods for anesthesia depth monitoring is using electroencephalogram (EEG). The bispectral index (BIS) is the most commonly used parameter for anesthesia depth monitoring using EEG, the validity of which is still to be studied before being accepted as a routine method by clinicians. This paper proposes a new technique for detecting the depth of anesthesia by means of EEG, which is based on multioutput least-squares support vector regression (MLS-SVR), which provides the probability that the patient is in the four different possible anesthesia states. In this study, EEG signals were recorded from 20 patients who were anesthetized in the operation room. Twelve linear and nonlinear EEG features were then extracted every 10 s from the EEG signals to form the feature vector. The features were then classified by the MLS-SVR classifier 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 shows a higher accuracy compared to the other previously reported methods.