北京生物医学工程

基于替代检验的部分相位同步指数的脑电信号分析

Partial phase synchronization analysis of EEGsignals based on surrogate tests

作者：李勇华戴加飞李锦王俊侯凤贞

单位：南京邮电大学图像处理与图像通信江苏省重点实验室(南京 210003)

分类号：R318.04

出版年·卷·期（页码）：2017·36·2（146-151）

摘要：

目的针对脑电数据的部分相位同步指数(partial phase synchronization index, PPSI)对数据长度的敏感性而引起计算PPSI时的不准确性等问题，本文提出基于替代检验算法以分析PPSI，进而找到计算PPSI时最优的数据长度。方法基于4种常见的替代数据生成法对10组脑电数据分别生成替代数据，并分析其显著性阈值与原始数据的PPSI的相关系数，选出最合适的替代数据生成法，然后基于选出的替代数据生成法分析脑电数据在多个长度下PPSI的显著性。结果结果显示RSS (rank-shuffled surrogate)算法得出的阈值与原始脑电信号的PPSI的相关系数最小，在3～18个周期长度下数据的阈值显著性在一个合理范围内。结论基于RSS算法更适合于脑电信号的PPSI的分析，并且发现3～18个周期长度下的脑电数据更适合分析PPSI。

Objective The partial phase synchronization index (PPSI) of EEG is sensitive to the data length, which causes the inaccuracy of PPSI.Aiming at this problem, we come up with the algorithm based on surrogate test to analyze the PPSI, and then find the best data length when calculating PPSI.Methods Firstly, we generate surrogate data for the 10 groups of EEG data based on four common surrogate data generation method, and analyze the correlation coefficient of significance threshold and the PPSI of original data to select the most suitable generating method for surrogate data.Then, PPSI significance is analyzed with EEG data of different lengths by the most suitable surrogate data algorithm.Results The results show that the correlation coefficient of the threshold based on RSS (rank-shuffled surrogate) algorithm and the original EEG PPSI is the minimum.Threshold significance is within a reasonable range when the data length is 3 to 18 cycles.Conclusions RSS algorithm is the most suitable surrogate data algorithm for the analysis of EEG PPSI, and the length of EEG data as 3 to 18 cycles is more suitable for analyzing PPSI.

参考文献：

［1］Serletis D, Carlen PL, Valiante TA, et al. Phase synchronization of neuronal noise in mouse hippocampal epileptiform dynamics ［J］. International Journal of Neural Systems, 2013, 23(1):99-107.

［2］Lai YC, Frei MG, Osorio I. Detecting and characterizing phase synchronization in nonstationary dynamical systems［J］. Physical Review E, 2006, 73(2): 026214-026224.

［3］Schelter B, Winterhalder M, Dahlhaus R, et al. Partial phase synchronization for multivariate synchronizing systems［J］. Physical review letters, 2006, 96(20): 208103-208300.

［4］Belluscio MA, Mizuseki K, Schmidt R, et al. Cross-frequency phase-phase coupling between theta and gamma oscillations in the hippocampus［J］. The Journal of neuroscience, 2012, 32(2): 423-435.

［5］Vázquez RR, Velez-Perez H, Ranta R, et al. Blind source separation, wavelet denoising and discriminant analysis for EEG artefacts and noise cancelling［J］. Biomedical Signal Processing and Control, 2012, 7(4): 389-400.

［6］Theiler J, Eubank S, Longtin A, et al. Testing for nonlinearity in time series: the method of surrogate data［J］. Physica D: Nonlinear Phenomena, 1992, 58(1-4): 77-94.

［7］Dolan KT, Spano ML. Surrogate for nonlinear time series analysis［J］. Physical Review E, 2001, 64(4): 046128.

［8］Sun J, Hong X, Tong S. Phase synchronization analysis of EEG signals: an evaluation based on surrogate tests［J］. Biomedical Engineering, IEEE Transactions on, 2012, 59(8): 2254-2263.

［9］Schelter B, Winterhalder M, Timmer J, et al. Testing for phase synchronization［J］. Physics Letters A, 2007, 366(4): 382-390.

［10］Rosenblum MG, Pikovsky AS, Kurths J. Phase synchronization of chaotic oscillators ［J］. Physical Review Letters, 1996, 76(11): 1804.

［11］Andrade V, Davidchack RL, Lai YC. Noise scaling of phase synchronization of chaos［J］. Physical Review E Statistical Physics Plasmas Fluids & Related Interdisciplinary Topics, 2000, 61(3): 3230-3233.

［12］Matheny MH, Grau M, Villanueva LG, et al. Phase synchronization of two anharmonic nanomechanical oscillators［J］. Physical Review Letters, 2014, 112(1): 014101.

［13］Mahmoud EE. Modified projective phase synchronization of chaotic complex nonlinear systems［J］. Mathematics and Computers in Simulation, 2013, 89：69-85.

［14］Schreiber T, Schmitz A. Surrogate time series［J］. Physica D: Nonlinear Phenomena, 2000, 142(3): 346-382.

［15］Delorme A, Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis［J］. Journal of Neuroscience Methods, 2004, 134(1): 9-21.

［16］Van de Ville D, Britz J, Michel CM. EEG microstate sequences in healthy humans at rest reveal scale-free dynamics［J］. Proceedings of the National Academy of Sciences, 2010, 107(42): 18179-18184.

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