Huang L, Yu P, Ju F, Cheng J
Department of Biomedical Engineering, Xi'an Jiaotong University, Xi'an, China 710049.
Med Eng Phys. 2003 May;25(4):321-7. doi: 10.1016/s1350-4533(02)00249-7.
This paper presents a new approach to predict response during isoflurane anaesthesia by using mutual information (MI) time series of electroencephalograms (EEGs) and their complexity analysis. The MI between four lead electrodes was first computed using the EEG time series. The Lempel-Ziv complexity measures, C(n)s, were extracted from the MI time series. Prediction was made by means of artificial neural network (ANN). From 98 consenting patient experiments, 98 distinct EEG recordings were collected prior to incision during isoflurane anaesthesia of different levels. During and after skin incision, each patient was observed carefully for 2 min to detect subsequent responses (purposeful movement, changes in hemodynamic parameters and respiratory pattern) and then the EEG was labelled as 0.0 for responder or as 1.0 for non-responder. Training and testing the ANN used the 'drop-one-patient' method. The prediction was tested by monitoring the response to incision and the result given by the ANN. The system was able to correctly classify purposeful response in average accuracy of 91.84% of the cases. The results showed that the method has a better performance than other methods, such as spectral edge frequency, median frequency, and bispectral analysis. This method is computationally fast and acceptable real-time clinical performance was obtained.
本文提出了一种新方法，通过使用脑电图（EEG）的互信息（MI）时间序列及其复杂度分析来预测异氟烷麻醉期间的反应。首先使用EEG时间序列计算四个导联电极之间的MI。从MI时间序列中提取Lempel-Ziv复杂度度量C(n)。通过人工神经网络（ANN）进行预测。在98例同意参与的患者实验中，在不同水平的异氟烷麻醉下，于切口前收集了98份不同的EEG记录。在皮肤切口期间及之后，仔细观察每位患者2分钟，以检测随后的反应（有目的的运动、血流动力学参数和呼吸模式的变化），然后将EEG标记为0.0表示有反应者，或标记为1.0表示无反应者。使用“留一法”对ANN进行训练和测试。通过监测对切口的反应以及ANN给出的结果来测试预测。该系统能够以91.84%的平均准确率正确分类有目的的反应。结果表明，该方法比其他方法，如频谱边缘频率、中位频率和双谱分析，具有更好的性能。该方法计算速度快，并获得了可接受的实时临床性能。
