Multiscale Lempel–Ziv complexity for EEG measures

OBJECTIVE To demonstrate that the classical calculation of Lempel-Ziv complexity (LZC) has an important limitation when applied to EEGs with rapid rhythms, and to propose a multiscale approach that overcomes this limitation. METHODS We have evaluated, both with simulated and real EEGs, whether LZC calculation neglects functional characteristics of rapid EEG rhythms. In addition, we have proposed a procedure to obtain multiple binarization sequences that yield a spectrum of LZC, and we have explored whether complexity would be better captured using this computation. RESULTS In our simulated signals, classical LZC did not capture modulations of a rapid component when a slower component of more amplitude was included in the signal. In real EEGs from healthy participants with eyes closed and eyes open, classical LZC calculation failed to show any difference between these two conditions. However, a multiscale LZC showed that complexity was lower for eyes closed than for eyes open conditions. CONCLUSIONS As hypothesized, our new approximation captures the complexity of series with fast components masked by slower rhythms. SIGNIFICANCE The method we introduce significantly improves LZC calculation, and it allows a better characterization of complexity of EEG signals.

[1]  T. Mizuno,et al.  Age-related variation in EEG complexity to photic stimulation: A multiscale entropy analysis , 2009, Clinical Neurophysiology.

[2]  Christopher R. Brown,et al.  EEG differences in children between eyes-closed and eyes-open resting conditions , 2009, Clinical Neurophysiology.

[3]  S. Tong,et al.  Abnormal EEG complexity in patients with schizophrenia and depression , 2008, Clinical Neurophysiology.

[4]  Schuster,et al.  Easily calculable measure for the complexity of spatiotemporal patterns. , 1987, Physical review. A, General physics.

[5]  Roberto Hornero,et al.  Lempel–Ziv complexity in schizophrenia: A MEG study , 2011, Clinical Neurophysiology.

[6]  Dezhong Yao,et al.  EEG Scaling Difference Between Eyes-Closed and Eyes-Open Conditions by Detrended Fluctuation Analysis , 2008 .

[7]  Andrzej Cichocki,et al.  Slowing and Loss of Complexity in Alzheimer's EEG: Two Sides of the Same Coin? , 2011, International journal of Alzheimer's disease.

[8]  G. Buzsáki,et al.  Neuronal Oscillations in Cortical Networks , 2004, Science.

[9]  C. Stam,et al.  Nonlinear dynamical analysis of EEG and MEG: Review of an emerging field , 2005, Clinical Neurophysiology.

[10]  Ray J. Solomonoff,et al.  A Formal Theory of Inductive Inference. Part II , 1964, Inf. Control..

[11]  Koichi Takahashi,et al.  Antipsychotics reverse abnormal EEG complexity in drug-naive schizophrenia: A multiscale entropy analysis , 2010, NeuroImage.

[12]  Ray J. Solomonoff,et al.  A Formal Theory of Inductive Inference. Part I , 1964, Inf. Control..

[13]  Erik W. Jensen,et al.  EEG complexity as a measure of depth of anesthesia for patients , 2001, IEEE Trans. Biomed. Eng..

[14]  Madalena Costa,et al.  Multiscale entropy analysis of biological signals. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[15]  Roberto Hornero,et al.  Interpretation of the Lempel-Ziv Complexity Measure in the Context of Biomedical Signal Analysis , 2006, IEEE Transactions on Biomedical Engineering.

[16]  K Lehnertz,et al.  Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[17]  S. Holm A Simple Sequentially Rejective Multiple Test Procedure , 1979 .

[18]  S. Baron-Cohen,et al.  Atypical EEG complexity in autism spectrum conditions: A multiscale entropy analysis , 2011, Clinical Neurophysiology.

[19]  Roberto Hornero,et al.  Complexity analysis of the magnetoencephalogram background activity in Alzheimer's disease patients. , 2006, Medical engineering & physics.

[20]  Men-Tzung Lo,et al.  Revealing the brain's adaptability and the transcranial direct current stimulation facilitating effect in inhibitory control by multiscale entropy , 2014, NeuroImage.

[21]  Abraham Lempel,et al.  On the Complexity of Finite Sequences , 1976, IEEE Trans. Inf. Theory.

[22]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[23]  A. Kolmogorov Three approaches to the quantitative definition of information , 1968 .

[24]  Radhakrishnan Nagarajan,et al.  Quantifying physiological data with Lempel-Ziv complexity-certain issues , 2002, IEEE Transactions on Biomedical Engineering.

[25]  Steven M. Pincus Approximate entropy as a measure of irregularity for psychiatric serial metrics. , 2006, Bipolar disorders.

[26]  R. Barry,et al.  EEG differences between eyes-closed and eyes-open resting conditions , 2007, Clinical Neurophysiology.

[27]  Madalena Costa,et al.  Multiscale entropy analysis of complex physiologic time series. , 2002, Physical review letters.

[28]  G. Bergey,et al.  Characterization of early partial seizure onset: Frequency, complexity and entropy , 2012, Clinical Neurophysiology.

[29]  J. Chambers,et al.  Introduction to EEG , 2013 .

[30]  T R Bashore,et al.  The algorithmic complexity of multichannel EEGs is sensitive to changes in behavior. , 2003, Psychophysiology.

[31]  D. Abásolo,et al.  Brain oscillatory complexity across the life span , 2012, Clinical Neurophysiology.

[32]  Daniel Abásolo,et al.  Non-linear analysis of EEG and MEG in patients with Alzheimer ’ s disease , 2008 .

[33]  Roberto Hornero,et al.  Nonlinear analysis of electroencephalogram and magnetoencephalogram recordings in patients with Alzheimer's disease , 2009, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.