基于模糊神经网络的麻醉深度的监测研究

Monitoring the Depth of Anesthesia Using a Fuzzy Neural Network Based on EEG

提出模糊神经网络对麻醉信号进行信息融合,实现麻醉深度监测的方法。实验是从31例复合全麻病人的EEG信号中提取出非线性动力学参数——KC复杂度、近似熵,和25例训练样本的训练及6例检验样本的前瞻性检验,结果表明以EEG信号的非线性动力学参数为输入的ANFIS网络输出具有显著的差异性,可以作为一种反映麻醉深度的定量指标。

In this paper, a fuzzy neural network （FNN） is proposed for fusing the anesthesia information, and realizing the monitonng 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.