Ordinal patterns in epileptic brains: Analysis of intracranial EEG and simultaneous EEG-fMRI

Epileptic seizures are associated with high behavioral stereotypy of the patients. In the EEG of epilepsy patients characteristic signal patterns can be found during and between seizures. Here we use ordinal patterns to analyze EEGs of epilepsy patients and quantify the degree of signal determinism. Besides relative signal redundancy and the fraction of forbidden patterns we introduce the fraction of under-represented patterns as a new measure. Using the logistic map, parameter scans are performed to explore the sensitivity of the measures to signal determinism. Thereafter, application is made to two types of EEGs recorded in two epilepsy patients. Intracranial EEG shows pronounced determinism peaks during seizures. Finally, we demonstrate that ordinal patterns may be useful for improving analysis of non-invasive simultaneous EEG-fMRI.

[1]  Kaspar Anton Schindler,et al.  On seeing the trees and the forest: Single‐signal and multisignal analysis of periictal intracranial EEG , 2012, Epilepsia.

[2]  F. Mormann,et al.  Seizure prediction: the long and winding road. , 2007, Brain : a journal of neurology.

[3]  Andreas Groth.  Visualization of coupling in time series by order recurrence plots. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[4]  Selim R Benbadis,et al.  Death and epilepsy , 2009, Expert review of neurotherapeutics.

[5]  Emery N Brown,et al.  Heterogeneous neuronal firing patterns during interictal epileptiform discharges in the human cortex. , 2010, Brain : a journal of neurology.

[6]  Kaspar Anton Schindler,et al.  Peri-ictal correlation dynamics of high-frequency (80–200Hz) intracranial EEG , 2010, Epilepsy Research.

[7]  Andreas Galka,et al.  Topics in Nonlinear Time Series Analysis, with Implications for Eeg Analysis , 2000 .

[8]  Dirk Hoyer,et al.  Permutation entropy improves fetal behavioural state classification based on heart rate analysis from biomagnetic recordings in near term fetuses , 2006, Medical and Biological Engineering and Computing.

[9]  James Theiler,et al.  Testing for nonlinearity in time series: the method of surrogate data , 1992 .

[10]  F. Mormann,et al.  Seizure prediction: Any better than chance? , 2009, Clinical Neurophysiology.

[11]  Dieter Schmidt,et al.  Modern management of epilepsy: A practical approach , 2008, Epilepsy & Behavior.

[12]  L. Spinelli,et al.  Volumetric measurements of subcortical nuclei in patients with temporal lobe epilepsy , 2001, Neurology.

[13]  O. Muzik,et al.  Glucose and [11C]flumazenil positron emission tomography abnormalities of thalamic nuclei in temporal lobe epilepsy , 1999, Neurology.

[14]  Thomas Dierks,et al.  BOLD correlates of continuously fluctuating epileptic activity isolated by independent component analysis , 2008, NeuroImage.

[15]  O A Rosso,et al.  Distinguishing noise from chaos. , 2007, Physical review letters.

[16]  Miguel A. F. Sanjuán,et al.  Combinatorial detection of determinism in noisy time series , 2008 .

[17]  Karsten Keller,et al.  Symbolic Analysis of High-Dimensional Time Series , 2003, Int. J. Bifurc. Chaos.

[18]  Rodrigo Quian Quiroga,et al.  Nonlinear multivariate analysis of neurophysiological signals , 2005, Progress in Neurobiology.

[19]  Kaspar Anton Schindler,et al.  Forbidden ordinal patterns of periictal intracranial EEG indicate deterministic dynamics in human epileptic seizures , 2011, Epilepsia.

[20]  H. Lüders,et al.  Presurgical evaluation of epilepsy. , 2001, Brain : a journal of neurology.

[21]  J. Winn,et al.  Brain , 1878, The Lancet.

[22]  Gaoxiang Ouyang,et al.  Deterministic dynamics of neural activity during absence seizures in rats. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[23]  Osvaldo A. Rosso,et al.  Bandt–Pompe approach to the classical-quantum transition , 2007 .

[24]  Ljupco Kocarev,et al.  Order patterns and chaos , 2006 .

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

[26]  Massimiliano Zanin,et al.  Permutation Entropy and Its Main Biomedical and Econophysics Applications: A Review , 2012, Entropy.

[27]  F. Leijten,et al.  EEG-fMRI in the preoperative work-up for epilepsy surgery. , 2007, Brain : a journal of neurology.

[28]  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.

[29]  Kaspar Anton Schindler,et al.  Localizing Seizure-Onset Zones in Presurgical Evaluation of Drug-Resistant Epilepsy by Electroencephalography/fMRI: Effectiveness of Alternative Thresholding Strategies , 2012, American Journal of Neuroradiology.

[30]  A. Villringer,et al.  Simultaneous EEG–fMRI , 2006, Neuroscience & Biobehavioral Reviews.

[31]  G. Mathern,et al.  Epilepsia , 1991, NEURO FUNDAMENTAL.

[32]  H. Lüders,et al.  Textbook of epilepsy surgery , 2008 .

[33]  T. Schreiber,et al.  Surrogate time series , 1999, chao-dyn/9909037.

[34]  H. Kantz,et al.  Nonlinear time series analysis , 1997 .

[35]  G. Buzsáki.  Rhythms of the brain , 2006 .

[36]  Sadri Hassani,et al.  Nonlinear Dynamics and Chaos , 2000 .

[37]  A. Pérez-Villalba.  Rhythms of the Brain, G. Buzsáki. Oxford University Press, Madison Avenue, New York (2006), Price: GB £42.00, p. 448, ISBN: 0-19-530106-4 , 2008 .

[38]  Cristina Masoller,et al.  Quantifying the complexity of the delayed logistic map , 2011, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[39]  J. A. Stewart,et al.  Nonlinear Time Series Analysis , 2015 .

[40]  Gaoxiang Ouyang,et al.  Ordinal pattern based similarity analysis for EEG recordings , 2010, Clinical Neurophysiology.

[41]  J. Gotman,et al.  Combining EEG and fMRI: A multimodal tool for epilepsy research , 2006, Journal of magnetic resonance imaging : JMRI.

[42]  B. Kendall.  Nonlinear Dynamics and Chaos , 2001 .

[43]  B. Pompe,et al.  Permutation entropy: a natural complexity measure for time series. , 2002, Physical review letters.

[44]  Manjit,et al.  Neurology , 1912, NeuroImage.

[45]  M. Crawford,et al.  Theory and methods , 1980 .

[46]  Josemir W Sander,et al.  The global burden and stigma of epilepsy , 2008, Epilepsy & Behavior.

[47]  Miguel A. F. Sanjuán,et al.  True and false forbidden patterns in deterministic and random dynamics , 2007 .