基于脑电样本熵和小波熵的麻醉深度监测
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摘要
目的:通过研究全麻手术病人的脑电信号特征,从分类准确率、算法难易程度、计算时间等方面讨论样本熵和小波熵算法在麻醉深度监测中的应用。方法:基于脑电信号的非线性和不稳定性,采用两种非线性动力学分析方法(样本熵和小波熵)对30例全麻手术病人的脑电信号进行特征提取,并对每位病人清醒状态、轻度麻醉状态和中度麻醉状态下的脑电信号的样本熵和小波熵进行差异分析。结果:不同麻醉状态下的脑电信号的样本熵和小波熵均有明显差异。相同脑电信号的样本熵的变化阈值较小波熵的变化阈值大。结论:样本熵和小波熵算法均可以作为麻醉深度监测的有效指标。从分类准确率、算法难易程度和计算时间等方面考虑,使用样本熵算法的效果优于小波熵算法。
Objective To research the characteristics of the electroencephalogram(EEG) signals of patients under general anesthesia, and to compare the performances of sample entropy and wavelet entropy algorithms in monitoring the depth of anesthesia, including classification accuracy, calculation complexity and calculation time. Methods Based on the characteristics of nonlinearity and instability of EEG signals, two kinds of nonlinear dynamics analysis methods, namely sample entropy algorithm and wavelet entropy algorithm, were used to extract the characteristics of the EEG signals of 30 patients under general anesthesia. The sample entropy and wavelet entropy of the EEG signals of patients under different anesthesia states(including waking state, light anesthesia and moderate anesthesia) were also compared with variance analysis. Results The sample entropy and wavelet entropy of the EEG signals under different states was significantly different. Moreover, the change threshold of sample entropy was larger than that of wavelet entropy. Conclusion Both sample entropy and wavelet entropy algorithms can be used as effective indicators for monitoring the depth of anesthesia, but when classification accuracy, calculation complexity and calculation time are taken into consideration, sample entropy algorithm is better than wavelet entropy algorithm.
Objective To research the characteristics of the electroencephalogram(EEG) signals of patients under general anesthesia, and to compare the performances of sample entropy and wavelet entropy algorithms in monitoring the depth of anesthesia, including classification accuracy, calculation complexity and calculation time. Methods Based on the characteristics of nonlinearity and instability of EEG signals, two kinds of nonlinear dynamics analysis methods, namely sample entropy algorithm and wavelet entropy algorithm, were used to extract the characteristics of the EEG signals of 30 patients under general anesthesia. The sample entropy and wavelet entropy of the EEG signals of patients under different anesthesia states(including waking state, light anesthesia and moderate anesthesia) were also compared with variance analysis. Results The sample entropy and wavelet entropy of the EEG signals under different states was significantly different. Moreover, the change threshold of sample entropy was larger than that of wavelet entropy. Conclusion Both sample entropy and wavelet entropy algorithms can be used as effective indicators for monitoring the depth of anesthesia, but when classification accuracy, calculation complexity and calculation time are taken into consideration, sample entropy algorithm is better than wavelet entropy algorithm.
引文
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[19]ZOUGHI T,BOOSTANI R,DEYPIR M.A wavelet-based estimating depth of anesthesia[J].Eng Appl Artif Intel,2012,25(8):1710-1722.
[20]CHEN J,DOU Y,LI Y,et al.Application of Shannon wavelet entropy and Shannon wavelet packet entropy in analysis of power system transient signals[J].Entropy,2016,18(12):437.
[21]QIAN Y,WANG J,JEON G,et al.Image deinterlacing using regionbased back propagation artificial neural network[J].Opt Eng,2013,52(7):073107.
[22]LI X,LI D,LIANG Z,et al.Analysis of depth of anesthesia with Hilbert-Huang spectral entropy[J].Clin Neurophysiol,2008,119(11):2465-2475.
[2]洪文学,张仲鹏,宋佳霖,等.麻醉深度监测方法及仪器研究的现状与展望[J].中国生物医学工程学报,2011,30(5):781-786.HONG W X,ZHANG Z P,SONG J L,et al.Current situation and prospect of the methods and instruments for monitoring the depth of anesthesia[J].Chinese Journal of Biomedical Engineering,2011,30(5):781-786.
[3]NGUYEN-KY T,WEN P,LI Y.Theoretical basis for identification of different anesthetic states based on routinely recorded EEG during operation[J].Comput Biol Med,2009,39(1):40-45.
[4]AL-KADI M I,REAZ M B,ALI M A.Evolution of electroencephalogram signal analysis techniques during anesthesia[J].Sensors,2013,13(5):6605-6635.
[5]BRUHN J,MYLES P S,SNEYD R,et al.Depth of anaesthesia monitoring:what's available,what's validated and what's next?[J].Br J Anaesth,2006,97(1):85-94.
[6]何庆华,廖维宏,吴宝明,等.脑电分析在麻醉深度监测中的应用[J].医疗卫生装备,2008,29(4):30-31.HE Q H,LIAO W H,WU B M,et al.Application of EEG analysis in monitoring depth of anaesthesia[J].Chinese Medical Equipment Journal,2008,29(4):30-31.
[7]贾娜,张昊鹏,文爱东,等.临床麻醉深度监测方法的新进展[J].临床麻醉学杂志,2015,31(9):922-925.JIA N,ZHANG H P,WEN A D,et al.New advances in the method of clinical anesthesia depth monitoring[J].Journal of Clinical Anesthesiology,2015,31(9):922-925.
[8]聂鲁振.麻醉深度监测算法研究及其实现[D].哈尔滨:哈尔滨工业大学,2015.NIE L Z.Research on anesthesia depth monitoring algorithm and implemetation[D].Harbin:Harbin Institute of Technology,2015.
[9]LIU Q,CHEN Y F,FAN S Z,et al.EEG signals analysis using multiscale entropy for depth of anesthesia monitoring during surgery through artificial neural networks[J].Comput Math Methods Med,2015(1):53-57.
[10]GIFANI P,RABIEE H R,HASHEMI M H,et al.Optimal fractalscaling analysis of human EEG dynamic for depth of anesthesia quantification[J].J Franklin Inst,2007,344(3-4):212-229.
[11]邹倩.格子复杂性在麻醉深度监测中的应用研究[D].杭州:浙江大学,2016.ZOU Q.The application study of lattice complexity on monitoring the depth of anesthesia[D].Hang Zhou:Zhejiang University,2016.
[12]王胜军,郭云波,杨俊,等.复杂度、近似熵应用于麻醉深度监测中的实验研究[J].医疗卫生装备,2010,31(1):19-20.WANG S J,GOU Y B,YANG J,et al.Experiment research on complexity and Ap En in monitoring of anesthesia depth[J].Chinese Medical Equipment Journal,2010,31(1):19-20.
[13]LIANG Z,WANG Y,SUN X,et al.EEG entropy measures in anesthesia[J].Front Comput Neurosc,2015,9:16.
[14]WANG Y,LIANG Z,VOSS L J,et al.Multi-scale sample entropy of electroencephalography during sevoflurane anesthesia[J].J Clin Monit Comput,2014,28(4):409-417.
[15]AL-NASHASH H A,PAUL J S,ZIAI W C,et al.Wavelet entropy for subbands egmentation of EEG during injury and recovery[J].Ann Biomed Eng,2003,31(6):653-658.
[16]JIANG G J,FAN S Z,ABBOD M F,et al.Sample entropy analysis of EEG signals via artificial neural networks to model patients'consciousness level based on anesthesiologists experience[J].Biomed Res Int,2015,2015:343478.
[17]SHALBAF R,BEHNAM H,SLEIGH J W,et al.Monitoring the depth of anesthesia using entropy features and an artificial neural network[J].J Neurosci Meth,2013,218(1):17-24.
[18]周静,吴效明.基于样本熵的睡眠呼吸暂停综合征脑电研究[J].中国医学物理学杂志,2016,33(7):722-725.ZHOU J,WU X M.Electroencephalogram of sleep apnea syndrome based on sample entropy[J].Chinese Journal of Medical Physics,2016,33(7):722-725.
[19]ZOUGHI T,BOOSTANI R,DEYPIR M.A wavelet-based estimating depth of anesthesia[J].Eng Appl Artif Intel,2012,25(8):1710-1722.
[20]CHEN J,DOU Y,LI Y,et al.Application of Shannon wavelet entropy and Shannon wavelet packet entropy in analysis of power system transient signals[J].Entropy,2016,18(12):437.
[21]QIAN Y,WANG J,JEON G,et al.Image deinterlacing using regionbased back propagation artificial neural network[J].Opt Eng,2013,52(7):073107.
[22]LI X,LI D,LIANG Z,et al.Analysis of depth of anesthesia with Hilbert-Huang spectral entropy[J].Clin Neurophysiol,2008,119(11):2465-2475.