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.
目的 研究腦電雙頻指數（BIS）在小兒麻醉中的應用，為BIS在小兒臨床麻醉上的廣泛應用及提高小兒麻醉的安全性提供依據。 方法 2011年1月－3月擬行腹部外科手術的患兒60例，男39例，女21例；年齡1～4歲，美國麻醉醫師協會分級Ⅰ～Ⅱ級，隨機分為兩組，每組各30例。S組：七氟醚、瑞芬太尼和維庫溴銨維持麻醉；P組：丙泊酚、瑞芬太尼和維庫溴銨維持麻醉。采用BIS監測麻醉深度，將BIS控制在50 ± 5，記錄麻醉誘導前到手術探查期間不同時點的血流動力學參數及蘇醒、拔管時間。 結果 手術過程中P組血壓及心率明顯低于麻醉前水平（P＜0.05）。S組蘇醒迅速、完全，蘇醒時間與P組比較差異有統計學意義（P＜0.05）。S組的血流動力學穩定性優于P組，蘇醒時間、拔管時間相對較短。麻醉誘導前兩組的BIS值均為97 ± 1，意識消失時的BIS值為71 ± 2。BIS值為50 ± 5時，結果顯示手術過程中（T4、T5、T6、T7）兩組的心率、血壓都很平穩。 結論 BIS作為小兒麻醉鎮靜深度的監測指標有臨床意義。
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.
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.
目的：本研究旨在比較一種新的腦電參數-腦電非線性指數（ENI）與BIS在丙泊酚靶控輸注時預測鎮靜深度的能力。方法:選擇30例18～60歲，ASA Ⅰ～Ⅱ級，擬行擇期普外科手術患者。每一患者同時監測ENI和BIS。麻醉誘導給予丙泊酚靶控輸注，直至患者意識消失后給予芬太尼和羅庫溴銨行氣管插管。麻醉誘導過程中每30秒進行一次鎮靜評分（采用改良OAA/S評分），并記錄ENI和BIS值以及平均動脈壓（MAP）和心率（HR）。結果:ENI和BIS與鎮靜評分的相關性比MAP和HR高（r=0.90、0.93 vs r=0.77、0.27）。鎮靜過程（改良OAA/S評分）中ENI和BIS有很好的相關性(R2=0.828)。ENI和BIS預測鎮靜深度的能力優于MAP和HR。結論：ENI可提供與BIS相似的反映鎮靜深度的信息，能準確預測不同的鎮靜深度。
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.