Analysis and Feature Extraction of EEG Signals Induced by Anesthesia Monitoring Based on Wavelet Transform

Abstract

Anesthesia signal monitoring is a very important indicator in surgery, and the effective monitoring of anesthesia depth has been the goal of anesthesiologists and biomedical engineering experts in recent decades. First, the wavelet transform method is used to analyze the anesthesia monitoring EEG signals, and the extracted features are clustered by wavelet classifier to estimate the depth of anesthesia. Second, the characteristics of eigenvectors are constructed by a singular value decomposition based on wavelet transform coefficients. The extraction method extracts the characteristics of the mid-latency auditory evoked EEG under anesthesia. Finally, this paper collected a large amount of clinical data and established a clinical database of anesthesia depth. The experimental results show the effectiveness of the method.