Evaluation of selected recurrence measures in discriminating pre-ictal and inter-ictal periods from epileptic EEG data

We investigate the suitability of selected measures of complexity based on recurrence quantification analysis and recurrence networks for an identification of pre-seizure states in multi-day, multi-channel, invasive electroencephalographic recordings from five epilepsy patients. We employ several statistical techniques to avoid spurious findings due to various influencing factors and due to multiple comparisons and observe precursory structures in three patients. Our findings indicate a high congruence among measures in identifying seizure precursors and emphasize the current notion of seizure generation in large-scale epileptic networks. A final judgment of the suitability for field studies, however, requires evaluation on a larger database.

[1]  Florian Mormann,et al.  Seizure prediction , 2008, Scholarpedia.

[2]  J. Hyttinen,et al.  Characterization of dynamical systems under noise using recurrence networks: Application to simulated and EEG data , 2014 .

[3]  C. Elger,et al.  Seizure prediction by non‐linear time series analysis of brain electrical activity , 1998, The European journal of neuroscience.

[4]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[5]  A. Kraskov,et al.  On the predictability of epileptic seizures , 2005, Clinical Neurophysiology.

[6]  Jürgen Kurths,et al.  Complex network based techniques to identify extreme events and (sudden) transitions in spatio-temporal systems. , 2015, Chaos.

[7]  Stephen M. Myers,et al.  Seizure prediction: Methods , 2011, Epilepsy & Behavior.

[8]  K. Lehnertz,et al.  Spatial Distribution of Neuronal Complexity Loss in Neocortical Lesional Epilepsies , 2000, Epilepsia.

[9]  Stephan U Schuele,et al.  Intractable epilepsy: management and therapeutic alternatives , 2008, The Lancet Neurology.

[10]  Norbert Marwan,et al.  Selection of recurrence threshold for signal detection , 2008 .

[11]  Jürgen Kurths,et al.  Identifying complex periodic windows in continuous-time dynamical systems using recurrence-based methods. , 2010, Chaos.

[12]  G. Worrell,et al.  Recurrence based deterministic trends in EEG records of epilepsy patients , 2008, 2008 International Conference on Information Technology and Applications in Biomedicine.

[13]  Klaus Lehnertz,et al.  Evolving networks in the human epileptic brain , 2013, 1309.4039.

[14]  C. Elger,et al.  Spatio-temporal dynamics of the primary epileptogenic area in temporal lobe epilepsy characterized by neuronal complexity loss. , 1995, Electroencephalography and clinical neurophysiology.

[15]  J. Zbilut,et al.  Recurrence quantification in epileptic EEGs , 2001 .

[16]  Ralph G Andrzejak,et al.  Detecting determinism with improved sensitivity in time series: rank-based nonlinear predictability score. , 2014, Physical review. E, Statistical, nonlinear, and soft matter physics.

[17]  D. Ruelle,et al.  Recurrence Plots of Dynamical Systems , 1987 .

[18]  J Kurths,et al.  Recurrence analysis of strange nonchaotic dynamics. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[19]  Klaus Lehnertz,et al.  Testing the null hypothesis of the nonexistence of a preseizure state. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[20]  Klaus Lehnertz,et al.  Time-dependent degree-degree correlations in epileptic brain networks: from assortative to dissortative mixing , 2015, Front. Hum. Neurosci..

[21]  H. Stepan,et al.  Classifying healthy women and preeclamptic patients from cardiovascular data using recurrence and complex network methods , 2013, Autonomic Neuroscience.

[22]  Josemir W Sander,et al.  Standards for epidemiologic studies and surveillance of epilepsy , 2011, Epilepsia.

[23]  K. Lehnertz,et al.  Seizure prediction and the preseizure period , 2002, Current opinion in neurology.

[24]  Recent Advances in Predicting and Preventing Epileptic Seizures Edited by Ronald Tetzlaff, Christian E. Elgar and Klaus Lehnertz (290 pages, Publisher: World Scientific Publishing Co Pte Ltd) , 2014, Seizure.

[25]  U. Rajendra Acharya,et al.  Application of Recurrence Quantification Analysis for the Automated Identification of Epileptic EEG Signals , 2011, Int. J. Neural Syst..

[26]  J. Kurths,et al.  Complex network approach for recurrence analysis of time series , 2009, 0907.3368.

[27]  K. Lehnertz,et al.  State dependent properties of epileptic brain networks: Comparative graph–theoretical analyses of simultaneously recorded EEG and MEG , 2010, Clinical Neurophysiology.

[28]  Jesse Gilbert,et al.  Analysis: Theory and Practice , 2013 .

[29]  Ralph G Andrzejak,et al.  Nonrandomness, nonlinear dependence, and nonstationarity of electroencephalographic recordings from epilepsy patients. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[30]  F. Mormann,et al.  Seizure prediction for therapeutic devices: A review , 2016, Journal of Neuroscience Methods.

[31]  De-Shuang Huang,et al.  Advanced Intelligent Computing Theories and Applications , 2010, Lecture Notes in Computer Science.

[32]  Physics Letters , 1962, Nature.

[33]  Norbert Marwan,et al.  How to Avoid Potential Pitfalls in Recurrence Plot Based Data Analysis , 2010, Int. J. Bifurc. Chaos.

[34]  Tianqiao Zhu,et al.  Predicting Epileptic Seizure by Recurrence Quantification Analysis of Single-Channel EEG , 2008, ICIC.

[35]  Klaus Lehnertz,et al.  Improved statistical test for nonstationarity using recurrence time statistics. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[36]  F. Mormann,et al.  Improved spatial characterization of the epileptic brain by focusing on nonlinearity , 2006, Epilepsy Research.

[37]  F. L. D. Silva,et al.  EEG analysis: Theory and Practice , 1998 .

[38]  W. Press,et al.  Fast algorithm for spectral analysis of unevenly sampled data , 1989 .

[39]  J. Kurths,et al.  Recurrence-plot-based measures of complexity and their application to heart-rate-variability data. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[40]  Klaus Lehnertz,et al.  Discerning nonstationarity from nonlinearity in seizure-free and preseizure EEG recordings from epilepsy patients , 2003, IEEE Transactions on Biomedical Engineering.

[41]  Jürgen Kurths,et al.  Multivariate recurrence network analysis for characterizing horizontal oil-water two-phase flow. , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

[42]  Jürgen Kurths,et al.  Analysis of High-Resolution Microelectrode EEG Recordings in an Animal Model of Spontaneous Limbic Seizures , 2009, Int. J. Bifurc. Chaos.

[43]  Jürgen Kurths,et al.  How do global temperature drivers influence each other? , 2013 .

[44]  K. Lehnertz,et al.  The epileptic process as nonlinear deterministic dynamics in a stochastic environment: an evaluation on mesial temporal lobe epilepsy , 2001, Epilepsy Research.

[45]  Jonathan F. Donges,et al.  Complex Network Analysis of Recurrences , 2015 .

[46]  J. Martinerie,et al.  Anticipating epileptic seizures in real time by a non-linear analysis of similarity between EEG recordings. , 1999, Neuroreport.

[47]  J. B. Gao,et al.  Recurrence Time Statistics for Chaotic Systems and Their Applications , 1999 .

[48]  Klaus Lehnertz,et al.  Stochastic qualifiers of epileptic brain dynamics. , 2007, Physical review letters.

[49]  E J Ngamga,et al.  Distinguishing dynamics using recurrence-time statistics. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[50]  J. Kurths,et al.  Estimation of dynamical invariants without embedding by recurrence plots. , 2004, Chaos.

[51]  F. Mormann,et al.  Nonlinear EEG analysis in epilepsy: its possible use for interictal focus localization, seizure anticipation, and prevention. , 2001, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[52]  J. Kurths,et al.  Analytical framework for recurrence network analysis of time series. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[53]  J. Zbilut,et al.  Embeddings and delays as derived from quantification of recurrence plots , 1992 .

[54]  Lei Hong,et al.  Recurrence Network Analysis of the Synchronous EEG Time Series in Normal and Epileptic Brains , 2012, Cell Biochemistry and Biophysics.

[55]  Michael Small,et al.  Superfamily phenomena and motifs of networks induced from time series , 2008, Proceedings of the National Academy of Sciences.

[56]  C. Elger,et al.  Measuring nonstationarity by analyzing the loss of recurrence in dynamical systems. , 2002, Physical review letters.

[57]  C E Elger,et al.  Preoperative Evaluation for Epilepsy Surgery (Bonn Algorithm) , 2002, Zentralblatt fur Neurochirurgie.

[58]  Norbert Marwan,et al.  The geometry of chaotic dynamics — a complex network perspective , 2011, 1102.1853.

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

[60]  Jonathan F. Donges,et al.  Geometric detection of coupling directions by means of inter-system recurrence networks , 2012, 1301.0934.

[61]  Daowen Qiu,et al.  Advanced Intelligent Computing Theories and Applications: With Aspects of Theoretical and Methodological Issues , 2008 .

[62]  C. Elger,et al.  CAN EPILEPTIC SEIZURES BE PREDICTED? EVIDENCE FROM NONLINEAR TIME SERIES ANALYSIS OF BRAIN ELECTRICAL ACTIVITY , 1998 .

[63]  R. Quiroga,et al.  Stationarity of the EEG series , 1995 .

[64]  Laurent Heutte,et al.  Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues, Third International Conference on Intelligent Computing, ICIC 2007, Qingdao, China, August 21-24, 2007, Proceedings , 2007, ICIC.

[65]  Jari Hyttinen,et al.  Dynamics of intracranial electroencephalographic recordings from epilepsy patients using univariate and bivariate recurrence networks. , 2015, Physical review. E, Statistical, nonlinear, and soft matter physics.

[66]  Norbert Marwan,et al.  A historical review of recurrence plots , 2008, 1709.09971.

[67]  Klaus Lehnertz,et al.  Long-term variability of global statistical properties of epileptic brain networks. , 2010, Chaos.

[68]  Andrew Herzog,et al.  Neuroendocrinological aspects of epilepsy: Important issues and trends in future research , 2011, Epilepsy & Behavior.

[69]  Norbert Marwan,et al.  Analysing spatially extended high-dimensional dynamics by recurrence plots , 2014, 1411.6159.

[70]  Jürgen Kurths,et al.  Recurrence plots for the analysis of complex systems , 2009 .

[71]  K. Lehnertz,et al.  Neuronal Complexity Loss in Interictal EEG Recorded with Foramen Ovale Electrodes Predicts Side of Primary Epileptogenic Area in Temporal Lobe Epilepsy: A Replication Study , 1998, Epilepsia.

[72]  Jürgen Kurths,et al.  Recurrence networks—a novel paradigm for nonlinear time series analysis , 2009, 0908.3447.

[73]  N. Marwan,et al.  Recurrence quantification analysis : theory and best practices , 2015 .