Literature DB >> 21838792

Forbidden ordinal patterns of periictal intracranial EEG indicate deterministic dynamics in human epileptic seizures.

Kaspar Schindler1, Heidemarie Gast, Lennart Stieglitz, Alexander Stibal, Martinus Hauf, Roland Wiest, Luigi Mariani, Christian Rummel.   

Abstract

PURPOSE: Epileptic seizures typically reveal a high degree of stereotypy, that is, for an individual patient they are characterized by an ordered and predictable sequence of symptoms and signs with typically little variability. Stereotypy implies that ictal neuronal dynamics might have deterministic characteristics, presumably most pronounced in the ictogenic parts of the brain, which may provide diagnostically and therapeutically important information. Therefore the goal of our study was to search for indications of determinism in periictal intracranial electroencephalography (EEG) studies recorded from patients with pharmacoresistent epilepsy.
METHODS: We assessed the number of forbidden ordinal patterns of 110 periictal multichannel intracranial EEG studies of 16 patients. Ordinal patterns are derived from the rank order of short sequences of consecutive EEG values. Ordinal patterns are well suited for analyzing real-world time series, for they have low sensitivity for many forms of noise and are applicable to nonstationary data. Although Gaussian random dynamics generate all possible ordinal patterns for a given sequence length, deterministic dynamics typically manifest with less random and more regular signals that miss a certain number of all the possible ordinal patterns. These missing ordinal patterns are referred to as "forbidden ordinal patterns." In this study, the number of forbidden ordinal patterns n(fp) of an EEG signal was interpreted as an indication of determinism, when it was larger than the number of forbidden patterns occurring in amplitude adjusted Fourier transform surrogates. We computed n(fp) for each EEG signal in a time-resolved way by using a moving-window approach. Then we specifically investigated n(mean)(fp) denoting the average number of forbidden patterns across all EEG signals, and n(max)(fp), which represents the number of forbidden patterns occurring in the EEG signal with the largest n(fp) during the seizure-onset period. KEY
FINDINGS: The average number of forbidden patterns of all EEG signals, n(mean)(fp), typically first increased and then decreased during the seizures. However, these changes were not statistically significant relative to the preseizure time period. In contrast, n(max)(fp)typically increased significantly during the first third of the seizure period and then gradually decreased toward and beyond seizure termination. In those patients who became seizure free following surgery, a larger percentage of the EEG signals containing the maximal number of forbidden patterns during the seizure-onset period tended to be recorded from within the visually identified seizure-onset zones. SIGNIFICANCE: Our findings demonstrate a spatiotemporally limited shift of neuronal dynamics toward a more deterministic dynamic regimen, specifically pronounced during the seizure-onset period. Assessing the number of forbidden ordinal patterns of intracranial EEG provides quantitative and observer-independent information. We propose that it is at least partially complementary to classical visual EEG reading and may be diagnostically helpful to better delineate ictogenic parts of the brain. Wiley Periodicals, Inc.
© 2011 International League Against Epilepsy.

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Year:  2011        PMID: 21838792     DOI: 10.1111/j.1528-1167.2011.03202.x

Source DB:  PubMed          Journal:  Epilepsia        ISSN: 0013-9580            Impact factor:   5.864


  9 in total

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Authors:  Alberto Porta; Mathias Baumert; Dirk Cysarz; Niels Wessel
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2015-02-13       Impact factor: 4.226

Review 2.  Ordinal symbolic analysis and its application to biomedical recordings.

Authors:  José M Amigó; Karsten Keller; Valentina A Unakafova
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2015-02-13       Impact factor: 4.226

3.  Is human atrial fibrillation stochastic or deterministic?-Insights from missing ordinal patterns and causal entropy-complexity plane analysis.

Authors:  Konstantinos N Aronis; Ronald D Berger; Hugh Calkins; Jonathan Chrispin; Joseph E Marine; David D Spragg; Susumu Tao; Harikrishna Tandri; Hiroshi Ashikaga
Journal:  Chaos       Date:  2018-06       Impact factor: 3.642

4.  Optimal control based seizure abatement using patient derived connectivity.

Authors:  Peter N Taylor; Jijju Thomas; Nishant Sinha; Justin Dauwels; Marcus Kaiser; Thomas Thesen; Justin Ruths
Journal:  Front Neurosci       Date:  2015-06-03       Impact factor: 4.677

5.  Unveiling the complex organization of recurrent patterns in spiking dynamical systems.

Authors:  Andrés Aragoneses; Sandro Perrone; Taciano Sorrentino; M C Torrent; Cristina Masoller
Journal:  Sci Rep       Date:  2014-04-15       Impact factor: 4.379

6.  Resected Brain Tissue, Seizure Onset Zone and Quantitative EEG Measures: Towards Prediction of Post-Surgical Seizure Control.

Authors:  Christian Rummel; Eugenio Abela; Ralph G Andrzejak; Martinus Hauf; Claudio Pollo; Markus Müller; Christian Weisstanner; Roland Wiest; Kaspar Schindler
Journal:  PLoS One       Date:  2015-10-29       Impact factor: 3.240

7.  Seizure pathways change on circadian and slower timescales in individual patients with focal epilepsy.

Authors:  Gabrielle M Schroeder; Beate Diehl; Fahmida A Chowdhury; John S Duncan; Jane de Tisi; Andrew J Trevelyan; Rob Forsyth; Andrew Jackson; Peter N Taylor; Yujiang Wang
Journal:  Proc Natl Acad Sci U S A       Date:  2020-05-04       Impact factor: 11.205

8.  Ngram-derived pattern recognition for the detection and prediction of epileptic seizures.

Authors:  Amir Eftekhar; Walid Juffali; Jamil El-Imad; Timothy G Constandinou; Christofer Toumazou
Journal:  PLoS One       Date:  2014-06-02       Impact factor: 3.240

9.  Predicting Surgery Targets in Temporal Lobe Epilepsy through Structural Connectome Based Simulations.

Authors:  Frances Hutchings; Cheol E Han; Simon S Keller; Bernd Weber; Peter N Taylor; Marcus Kaiser
Journal:  PLoS Comput Biol       Date:  2015-12-10       Impact factor: 4.475

  9 in total

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