生物医学工程研究2026，Vol.45Issue(4)：266-272,7.DOI:10.19529/j.cnki.1672-6278.2026.04.01
基于时频脊与改进深度残差收缩网络的麻醉状态评估方法
Anesthesia state assessment method based on time-frequency ridge and improved deep residual shrinkage network
摘要
Abstract
To efficiently and transparently evaluate the anesthesia status of patients during the perioperative period,we proposed an improved deep residual shrinkage network(DRSN)model for real-time monitoring of anesthesia status based on electroencephalogram(EEG)signals and time-frequency ridge features.Firstly,time-frequency ridges were extracted from the five sub bands of EEG sig-nals,minimizing input data while maximally fusing time-frequency features.Then,by concatenating channels and temporal attention weights,the original EEG,time-frequency ridges and electrocardiogram(ECG)signals were fused.The spatial-temporal features in the data were enhanced through an improved DRSN.Finally,the weight loss function composed of label distribution smoothing strategy was introduced to alleviate the problem of imbalanced data distribution in depth of anesthesia(DoA)assessment.The results showed that the Pearson correlation coefficient of the EEG bispectral index(BIS)reached 0.890 in the regression task for anesthesia state as-sessment,and the accuracy of anesthesia state prediction in classification tasks reached 0.925.This study can provide reference for as-sisting anesthesiologists in making decisions and enhance perioperative safety.关键词
麻醉状态评估/时频脊/深度学习/脑电/脑电双频指数Key words
Anesthesia state assessment/Time-frequency ridge/Deep learning/Electroencephalogram/Bispectral index分类
医药卫生引用本文复制引用
王思博,李振锋,王鹏,吴庞,杜利东,方震..基于时频脊与改进深度残差收缩网络的麻醉状态评估方法[J].生物医学工程研究,2026,45(4):266-272,7.基金项目
国家自然科学基金重点项目(62401547) （62401547）
国家自然科学基金项目(62371441) （62371441）
北京市自然科学基金-小米创新联合基金项目(L253012) （L253012）
