[Monitoring the depth of anesthesia using a fuzzy neural network based on EEG].
In this paper, a fuzzy neural network (FNN) is proposed for fusing the anesthesia information, and realizing the monitoring of the depth of anesthesia (DOA). EEG data from 31 patients undergoing general anesthesia with different anesthetic agents, and Kc complexity (Kc), approximate entropy (ApEn) were extracted and the fuzzy neural network was trained by 25 samples, and tested by the other 6 samples. The results show that the outputs of the fuzzy neural network whose inputs were Kc and ApEn obtained under the awake state and asleep state, exist obvious difference. It can be regarded as an quantitative index to estimate DOA.
Li
Min
M
Dept. of Biomedical Engineering, Zhejiang University, Hangzhou, 310027.
Ye
Zhi-qian
ZQ
D000465:Algorithms; D000758:Anesthesia / Q000379:methods*; D003198:Computer Simulation; D004569:Electroencephalography; D017143:Fuzzy Logic; D006801:Humans; D016343:Monitoring, Intraoperative / Q000379:methods; D008991:Monitoring, Physiologic / Q000379:methods; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D012815:Signal Processing, Computer-Assisted