Koskinen Miika, Mustola Seppo, Seppäinen Tapio
Department of Electrical and Information Engineering, University of Oulu, FIN-90014 Oulu, Finland.
IEEE Trans Biomed Eng. 2006 Oct;53(10):2008-14. doi: 10.1109/TBME.2006.881786.
The objective of this study is to model the association between the electroencephalogram (EEG) spectral features and the novel r scale representing the sedative effects of the propofol anesthetic drug. On the basis of the r scale, the unresponsiveness to the verbal command (LVC) is forecasted. EEG recordings are taken from a 16-patient study population undergoing propofol anesthetic induction. EEG was filtered into consecutive 4-Hz passbands up to 28 Hz. Of these time-series, the amplitude envelopes were extracted and used as input features to the first and the second-order polynomial multiple linear regression models. The values r epsilon [0.4, 1] were predicted with the R2 value of 0.775 with a cross validation. The LVC times were forecasted with the median error of 5%-7% or equivalently 10-13 s. In contrast, using the median of the measured LVC times of the training population as a forecast, the corresponding error was 12% or 26 s. The results suggest an acceptable correlation between the r scale and the EEG spectrum in the studied range. Moreover, the r values of an individual can be predicted using a population model. The suggested framework enables forecasting the LVC, which may open new possibilities for steering the drug administration.
本研究的目的是建立脑电图（EEG）频谱特征与代表丙泊酚麻醉药物镇静效果的新型r量表之间的关联模型。基于r量表，预测对言语指令无反应（LVC）的情况。EEG记录取自16名接受丙泊酚麻醉诱导的患者。EEG被滤波成高达28 Hz的连续4 Hz通带。在这些时间序列中，提取幅度包络并将其用作一阶和二阶多项式多元线性回归模型的输入特征。通过交叉验证，预测r值在[0.4, 1]范围内，R2值为0.775。预测LVC时间的中位数误差为5%-7%，即相当于10-13秒。相比之下，将训练人群中测量的LVC时间的中位数用作预测，相应误差为12%或26秒。结果表明，在所研究的范围内，r量表与EEG频谱之间存在可接受的相关性。此外，可以使用群体模型预测个体的r值。所建议的框架能够预测LVC，这可能为指导药物给药开辟新的可能性。
