Propofol Anesthesia Depth Monitoring Based on Self-Attention and Residual Structure Convolutional Neural Network.

Journal: Computational and mathematical methods in medicine
PMID: 35132332

Abstract

METHODS: We compare nine index values, select CNN+EEG, which has good correlation with BIS index, as an anesthesia state observation index to identify the parameters of the model, and establish a model based on self-attention and dual resistructure convolutional neural network. The data of 93 groups of patients were selected and randomly grouped into three parts: training set, validation set, and test set, and compared the best and worst results predicted by BIS.

Authors

  • Yachao Wang
    Dept. Anesthesiol, First People's Hospital Xiaoshan, Hangzhou, Zhejiang, China.
  • Hui Zhang
    Department of Pulmonary Vessel and Thrombotic Disease, Sixth Medical Center, Chinese PLA General Hospital, Beijing, China.
  • Ying Fan
    Division of Clinical Review, Office of Safety and Clinical Evaluation, Office of Generic Drugs, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, United States.
  • Peng Ying
    Department of Anesthesiology, Shulan (Hangzhou) Hospital Affiliated to Zhejiang Shuren University Shulan International Medical College, China.
  • Jun Li
    Department of Emergency, Zhuhai Integrated Traditional Chinese and Western Medicine Hospital, Zhuhai, 519020, Guangdong Province, China. quanshabai43@163.com.
  • Chenyao Xie
    Dept. Anesthesiol, First People's Hospital Xiaoshan, Hangzhou, Zhejiang, China.
  • Tingting Zhao
    School of Software Engineering, Beihang University, Beijing, China.

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