Abstract

Info

Title:: Estimating depth of anesthesia based on analysis of multi-domain EEG parameters

Author(s):: YU Chenyou; CHENG Yunzhang; School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China

Keywords:: Keywords: depth of anesthesia electroencephalogram signal random forest

Abstract:: Abstract: A method that combines the output of random forest model and EEG parameters is proposed to estimate the depth of anesthesia, thereby improving the reliability of estimating the depth of anesthesia. The EEG signal is divided into multiple segments of equal length after filtering. Ten parameters in the nonlinear domain, frequency domain, and time domain are extracted from each EEG signal segment for constituting the EEG parameter-BIS value data set. Then, a random forest regression model for estimating the depth of anesthesia is established, and the parameter used to assist in the model evaluation is screened out of these EEG parameters. Finally, the performances of the model and the selected parameter in the estimation of the depth of anesthesia are verified on the test set. There are good consistency and correlation between the estimated value on the test set and the true value (Pearson correlation=0.975), and the selected parameter can also achieve a total accuracy of 82.3% on the test set, which shows that the proposed method has a high application value in estimating the depth of anesthesia.