Construction of Complex Brain Network Based on EEG Signals and Evaluation of General Anesthesia Status

Abstract

General anesthesia is now an important part of surgery, it can ensure that patients undergo surgery in a painless and unconscious state. The traditional anesthesia depth assessment mainly relies on the subjective judgment of the anesthesiologist, lacks a unified standard, and is prone to misjudgment. Since general anesthesia is essentially anesthesia of the central nervous system, the state of anesthesia can be monitored based on EEG analysis. Based on this, this paper proposes a method to reasonably construct a brain connection network system based on the characteristic parameters of EEG signals and combine machine learning to evaluate the state of anesthesia. This method extracts the EEG signals related to the depth of anesthesia, The knowledge of graph theory introduces the three functional indicators of Pearson correlation coefficient, phase-lock value and phase lag index to construct a complex brain network, and then perform feature selection based on the constructed brain network to generate a dataset, and use machine learning methods for classification. To evaluate the anesthesia state, the experimental results show that the accuracy of the method for evaluating the anesthesia state can reach 93.88%.