目的：本研究旨在比较一种新的脑电参数-脑电非线性指数（ENI）与BIS在丙泊酚靶控输注时预测镇静深度的能力。方法:选择30例18～60岁，ASA Ⅰ～Ⅱ级，拟行择期普外科手术患者。每一患者同时监测ENI和BIS。麻醉诱导给予丙泊酚靶控输注，直至患者意识消失后给予芬太尼和罗库溴铵行气管插管。麻醉诱导过程中每30秒进行一次镇静评分（采用改良OAA/S评分），并记录ENI和BIS值以及平均动脉压（MAP）和心率（HR）。结果:ENI和BIS与镇静评分的相关性比MAP和HR高（r=0.90、0.93 vs r=0.77、0.27）。镇静过程（改良OAA/S评分）中ENI和BIS有很好的相关性(R2=0.828)。ENI和BIS预测镇静深度的能力优于MAP和HR。结论：ENI可提供与BIS相似的反映镇静深度的信息，能准确预测不同的镇静深度。
目的 研究脑电双频指数（BIS）在小儿麻醉中的应用，为BIS在小儿临床麻醉上的广泛应用及提高小儿麻醉的安全性提供依据。 方法 2011年1月－3月拟行腹部外科手术的患儿60例，男39例，女21例；年龄1～4岁，美国麻醉医师协会分级Ⅰ～Ⅱ级，随机分为两组，每组各30例。S组：七氟醚、瑞芬太尼和维库溴铵维持麻醉；P组：丙泊酚、瑞芬太尼和维库溴铵维持麻醉。采用BIS监测麻醉深度，将BIS控制在50 ± 5，记录麻醉诱导前到手术探查期间不同时点的血流动力学参数及苏醒、拔管时间。 结果 手术过程中P组血压及心率明显低于麻醉前水平（P＜0.05）。S组苏醒迅速、完全，苏醒时间与P组比较差异有统计学意义（P＜0.05）。S组的血流动力学稳定性优于P组，苏醒时间、拔管时间相对较短。麻醉诱导前两组的BIS值均为97 ± 1，意识消失时的BIS值为71 ± 2。BIS值为50 ± 5时，结果显示手术过程中（T4、T5、T6、T7）两组的心率、血压都很平稳。 结论 BIS作为小儿麻醉镇静深度的监测指标有临床意义。
In the present study carried out in our laboratory, we recorded local field potential (LFP) signals in primary visual cortex (V1 area) of rats during the anesthesia process in the electrophysiological experiments of invasive microelectrode array implant, and obtained time evolutions of complexity measure Lempel-ziv complexity (LZC) by nonlinear dynamic analysis method. Combined with judgment criterion of tail flick latency to thermal stimulus and heart rate, the visual stimulation experiments are carried out to verify the reliability of anesthetized states by complexity analysis. The experimental results demonstrated that the time varying complexity measures LZC of LFP signals of different channels were similar to each other in the anesthesia process. In the same anesthesia state, the difference of complexity measure LZC between neuronal responses before and after visual stimulation was not significant. However, the complexity LZC in different anesthesia depths had statistical significances. Furthermore, complexity threshold value represented the depth of anesthesia was determined using optimization method. The reliability and accuracy of monitoring the depth of anesthesia using complexity measure LZC of LFP were all high. It provided an effective method of realtime monitoring depth of anesthesia for craniotomy patients in clinical operation.
Currently, monitoring system of awareness of the depth of anesthesia has been more and more widely used in clinical practices. The intelligent evaluation algorithm is the key technology of this type of equipment. On the basis of studies about changes of electroencephalography (EEG) features during anesthesia, a discussion about how to select reasonable EEG parameters and classification algorithm to monitor the depth of anesthesia has taken place. A scheme which combines time domain analysis, frequency domain analysis and the variability of EEG and decision tree as classifier and least squares to compute Depth of anesthesia Index (DOAI) is proposed in this paper. Using the EEG of 40 patients who underwent general anesthesia with propofol, and the classification and the score of the EEG annotated by anesthesiologist, we verified this scheme with experiments. Classification and scoring was based on a combination of modified observer assessment of alertness/sedation (MOAA/S), and the changes of EEG parameters of patients during anesthesia. Then we used the BIS index to testify the validation of the DOAI. Results showed that Pearson's correlation coefficient between the DOAI and the BIS over the test set was 0.89. It is demonstrated that the method is feasible and has good accuracy.
Objectives To investigate the association of anesthesia recovery time and bispectral index (BIS) monitoring after gastrointestinal surgeries under general anesthesia. Methods A total of 404 cases of selective gastrointestinal surgeries under general anesthesia with BIS monitoring in West China Hospital of Sichuan University from January 2016 to June 2016 were retrieved from anesthesia medical record system as BIS monitoring exposure cohort (group BIS). In addition, 404 cases of selective gastrointestinal surgeries without BIS monitoring were matched as none BIS monitoring exposure cohort (group non-BIS). The primary outcome was the anesthesia recovery time, including the time from the end of surgery to endotracheal extubation (t1) and exiting the operation room (t2). A sub-group analysis was conducted based on patients’ age, length of operation time (t0) and type of surgery(open surgeries vs laparoscopic surgeries). Results The gender, age, body weight and ASA categories between two groups had no significant differences (P>0.05). The length of operation time also had no significant differences between two groups (P>0.05). The extubation time (10.1±4.4vs. 16.4±6.8) and OR exiting time (21.7±12.3 vs. 27.4±14.6) in group BIS were shorter than those in group non-BIS (P<0.05). This difference was markedly significant among elderly patients (age>60) or patients undergoing long operations (operation time>5hours). Among each group, the recovery time had no significant difference between open surgeries and laparoscopic surgeries. Conclusions There is an association between BIS monitoring and shorter anesthesia recovery time in gastrointestinal surgery, including the time of endotracheal extubation and exiting the operation room. BIS monitoring enhances anesthesia recovery among elderly patients and patients undergoing long-lasting operations in particular. There is no significant difference in anesthesia recovery time between open surgeries and laparoscopic surgeries.
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.