Adaptive Multiscale Entropy Analysis of Multivariate Neural Data

被引:68
作者
Hu, Meng [1 ]
Liang, Hualou [1 ]
机构
[1] Drexel Univ, Sch Biomed Engn Sci & Hlth Syst, Philadelphia, PA 19104 USA
基金
美国国家卫生研究院;
关键词
Entropy; local field potential (LFP); multiple scale analysis; multivariate empirical mode decomposition (MEMD); SUPPRESSION;
D O I
10.1109/TBME.2011.2162511
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Multiscale entropy (MSE) has been widely used to quantify a system's complexity by taking into account the multiple time scales inherent in physiologic time series. The method, however, is biased toward the coarse scale, i.e., low-frequency components due to the progressive smoothing operations. In addition, the algorithm for extracting the different scales is not well adapted to nonlinear/nonstationary signals. In this letter, we introduce adaptive multiscale entropy (AME) measures in which the scales are adaptively derived directly from the data by virtue of recently developed multivariate empirical mode decomposition. Depending on the consecutive removal of low-frequency or high-frequency components, our AME can be estimated at either coarse-to-fine or fine-to-coarse scales over which the sample entropy is performed. Computer simulations are performed to verify the effectiveness of AME for analysis of the highly nonstationary data. Local field potentials collected from the visual cortex of macaque monkey while performing a generalized flash suppression task are used as an example to demonstrate the usefulness of our AME approach to reveal the underlying dynamics in complex neural data.
引用
收藏
页码:12 / 15
页数:4
相关论文
共 11 条
[1]   Intrinsic mode entropy for nonlinear discriminant analysis [J].
Amoud, Hassan ;
Snoussi, Hichem ;
Hewson, David ;
Doussot, Michel ;
Duchene, Jacques .
IEEE SIGNAL PROCESSING LETTERS, 2007, 14 (05) :297-300
[2]   Multiscale entropy analysis of biological signals [J].
Costa, M ;
Goldberger, AL ;
Peng, CK .
PHYSICAL REVIEW E, 2005, 71 (02)
[3]   Multiscale entropy analysis of complex physiologic time series [J].
Costa, M ;
Goldberger, AL ;
Peng, CK .
PHYSICAL REVIEW LETTERS, 2002, 89 (06) :1-068102
[4]   The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis [J].
Huang, NE ;
Shen, Z ;
Long, SR ;
Wu, MLC ;
Shih, HH ;
Zheng, QN ;
Yen, NC ;
Tung, CC ;
Liu, HH .
PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, 1998, 454 (1971) :903-995
[5]   Sample entropy analysis of neonatal heart rate variability [J].
Lake, DE ;
Richman, JS ;
Griffin, MP ;
Moorman, JR .
AMERICAN JOURNAL OF PHYSIOLOGY-REGULATORY INTEGRATIVE AND COMPARATIVE PHYSIOLOGY, 2002, 283 (03) :R789-R797
[6]   APPROXIMATE ENTROPY AS A MEASURE OF SYSTEM-COMPLEXITY [J].
PINCUS, SM .
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 1991, 88 (06) :2297-2301
[7]   Multivariate empirical mode decomposition [J].
Rehman, N. ;
Mandic, D. P. .
PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, 2010, 466 (2117) :1291-1302
[8]   Filter Bank Property of Multivariate Empirical Mode Decomposition [J].
Rehman, Naveed Ur ;
Mandic, Danilo P. .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2011, 59 (05) :2421-2426
[9]  
Richman JS, 2000, AM J PHYSIOL-HEART C, V278, pH2039
[10]   Generalized flash suppression of salient visual targets [J].
Wilke, M ;
Logothetis, NK ;
Leopold, DA .
NEURON, 2003, 39 (06) :1043-1052