Jordan Denis, Stockmanns Gudrun, Kochs Eberhard F, Pilge Stefanie, Schneider Gerhard
Department of Anesthesiology, Klinikum rechts der Isar, Technische Universität München, Germany.
Anesthesiology. 2008 Dec;109(6):1014-22. doi: 10.1097/ALN.0b013e31818d6c55.
Nonlinear electroencephalographic parameters, e.g., approximate entropy, have been suggested as measures of the hypnotic component of anesthesia. Compared with linear methods, they may detect additional information and quantify the irregularity of a dynamical system. High dimensionality of a signal and disturbances may affect these parameters and change their ability to distinguish consciousness from unconsciousness. Methods of order pattern analysis, in this investigation represented by permutation entropy, recurrence rate, and phase coupling of order recurrence plots, are suitable for any type of time series, whether deterministic or noisy. They may provide a better estimation of the hypnotic component of anesthesia than other nonlinear parameters.
The current analysis is based on electroencephalographic data from two similar clinical studies in adult patients undergoing general anesthesia with sevoflurane or propofol. The study period was from induction until patients followed command after surgery, including a reduction of the hypnotic agent after tracheal intubation until patients followed command. Prediction probability was calculated to assess the parameter's ability to separate consciousness from unconsciousness at the transition between both states.
Parameters of order pattern analysis provide a prediction probability of maximal 0.85 (training study) and 0.78 (evaluation study) with frequencies from 0 to 30 Hz, and maximal 0.87 (training study) and 0.83 (evaluation study) including frequencies up to 70 Hz, both higher than 0.77 (approximate entropy).
Parameters of the nonlinear method order pattern analysis separate consciousness from unconsciousness and are grossly independent of high-frequency components of the electroencephalogram.
非线性脑电图参数，如近似熵，已被提议作为麻醉催眠成分的测量指标。与线性方法相比，它们可能检测到额外信息并量化动态系统的不规则性。信号的高维度和干扰可能会影响这些参数，并改变它们区分意识和无意识的能力。阶次模式分析方法，在本研究中以排列熵、复发率和阶次复发图的相位耦合为代表，适用于任何类型的时间序列，无论是确定性的还是有噪声的。它们可能比其他非线性参数能更好地估计麻醉的催眠成分。
当前分析基于两项相似的临床研究的脑电图数据，这些研究针对接受七氟醚或丙泊酚全身麻醉的成年患者。研究期从诱导期开始，直至患者术后能听从指令，包括气管插管后减少催眠药物剂量直至患者能听从指令。计算预测概率以评估参数在两种状态转换时区分意识和无意识的能力。
阶次模式分析参数在频率为0至30Hz时，预测概率最高为0.85（训练研究）和0.78（评估研究），在频率高达70Hz时，预测概率最高为0.87（训练研究）和0.83（评估研究），两者均高于0.77（近似熵）。
非线性方法阶次模式分析的参数能区分意识和无意识，且大致独立于脑电图的高频成分。
