基于小波变换与人工神经网络的麻醉深度计算方法研究

An anesthesia depth computing method study based on wavelet transform and artificial neural network

全身麻醉是外科手术中保证患者安全的必不可少的部分,脑电图(EEG)能反映大脑活动状况,包含丰富的信息,因此已广泛应用于监测麻醉深度。本文提出了一种将小波变换与人工神经网络(ANN)相结合的方法来估计麻醉深度。利用离散小波变换(DWT)将脑电信号进行分解,根据分解得到的近似系数与细节系数计算9种特征参数,并对这9种特征参数进行克鲁斯卡尔-沃利斯统计检验,结果表明这9种特征参数在清醒、轻度麻醉、中度麻醉和深度麻醉这四种不同麻醉水平间的差异均有统计学意义(P <0.001)。将这9种特征参数作为ANN的输入,以双谱指数(BIS)作为参考输出,使用8例全麻手术的患者数据对该方法进行了评估。该方法在7∶3留出法中对测试集四种不同麻醉水平的分类准确度为85.98%,与BIS的相关系数为0.977 0。结果表明,该方法能较好地区分四种不同麻醉水平,对于麻醉深度监测具有广阔的应用前景。

General anesthesia is an essential part of surgery to ensure the safety of patients. Electroencephalogram(EEG) has been widely used in anesthesia depth monitoring for abundant information and the ability of reflecting the brain activity. The paper proposes a method which combines wavelet transform and artificial neural network(ANN) to assess the depth of anesthesia. Discrete wavelet transform was used to decompose the EEG signal, and the approximation coefficients and detail coefficients were used to calculate the 9 characteristic parameters. Kruskal-Wallis statistical test was made to these characteristic parameters, and the test showed that the parameters were statistically significant for the differences of the four levels of anesthesia: awake, light anesthesia, moderate anesthesia and deep anesthesia(P < 0.001).The 9 characteristic parameters were used as the input of ANN, the bispectral index(BIS) was used as the reference output, and the method was evaluated by the data of 8 patients during general anesthesia. The accuracy of the method in the classification of the four anesthesia levels of the test set in the 7:3 set-out method was 85.98%, and the correlation coefficient with the BIS was 0.977 0. The results show that this method can better distinguish four different anesthesia levels and has broad application prospects for monitoring the depth of anesthesia.