Cargando…

Multivariate Matching Pursuit Decomposition and Normalized Gabor Entropy for Quantification of Preictal Trends in Epilepsy

Quantification of the complexity of signals recorded concurrently from multivariate systems, such as the brain, plays an important role in the study and characterization of their state and state transitions. Multivariate analysis of the electroencephalographic signals (EEG) over time is conceptually...

Descripción completa

Detalles Bibliográficos
Autores principales: Liu, Rui, Karumuri, Bharat, Adkinson, Joshua, Hutson, Timothy Noah, Vlachos, Ioannis, Iasemidis, Leon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7512937/
https://www.ncbi.nlm.nih.gov/pubmed/33265509
http://dx.doi.org/10.3390/e20060419
_version_ 1783586272550322176
author Liu, Rui
Karumuri, Bharat
Adkinson, Joshua
Hutson, Timothy Noah
Vlachos, Ioannis
Iasemidis, Leon
author_facet Liu, Rui
Karumuri, Bharat
Adkinson, Joshua
Hutson, Timothy Noah
Vlachos, Ioannis
Iasemidis, Leon
author_sort Liu, Rui
collection PubMed
description Quantification of the complexity of signals recorded concurrently from multivariate systems, such as the brain, plays an important role in the study and characterization of their state and state transitions. Multivariate analysis of the electroencephalographic signals (EEG) over time is conceptually most promising in unveiling the global dynamics of dynamical brain disorders such as epilepsy. We employed a novel methodology to study the global complexity of the epileptic brain en route to seizures. The developed measures of complexity were based on Multivariate Matching Pursuit (MMP) decomposition of signals in terms of time–frequency Gabor functions (atoms) and Shannon entropy. The measures were first validated on simulation data (Lorenz system) and then applied to EEGs from preictal (before seizure onsets) periods, recorded by intracranial electrodes from eight patients with temporal lobe epilepsy and a total of 42 seizures, in search of global trends of complexity before seizures onset. Out of five Gabor measures of complexity we tested, we found that our newly defined measure, the normalized Gabor entropy (NGE), was able to detect statistically significant (p < 0.05) nonlinear trends of the mean global complexity across all patients over 1 h periods prior to seizures’ onset. These trends pointed to a slow decrease of the epileptic brain’s global complexity over time accompanied by an increase of the variance of complexity closer to seizure onsets. These results show that the global complexity of the epileptic brain decreases at least 1 h prior to seizures and imply that the employed methodology and measures could be useful in identifying different brain states, monitoring of seizure susceptibility over time, and potentially in seizure prediction.
format Online
Article
Text
id pubmed-7512937
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-75129372020-11-09 Multivariate Matching Pursuit Decomposition and Normalized Gabor Entropy for Quantification of Preictal Trends in Epilepsy Liu, Rui Karumuri, Bharat Adkinson, Joshua Hutson, Timothy Noah Vlachos, Ioannis Iasemidis, Leon Entropy (Basel) Article Quantification of the complexity of signals recorded concurrently from multivariate systems, such as the brain, plays an important role in the study and characterization of their state and state transitions. Multivariate analysis of the electroencephalographic signals (EEG) over time is conceptually most promising in unveiling the global dynamics of dynamical brain disorders such as epilepsy. We employed a novel methodology to study the global complexity of the epileptic brain en route to seizures. The developed measures of complexity were based on Multivariate Matching Pursuit (MMP) decomposition of signals in terms of time–frequency Gabor functions (atoms) and Shannon entropy. The measures were first validated on simulation data (Lorenz system) and then applied to EEGs from preictal (before seizure onsets) periods, recorded by intracranial electrodes from eight patients with temporal lobe epilepsy and a total of 42 seizures, in search of global trends of complexity before seizures onset. Out of five Gabor measures of complexity we tested, we found that our newly defined measure, the normalized Gabor entropy (NGE), was able to detect statistically significant (p < 0.05) nonlinear trends of the mean global complexity across all patients over 1 h periods prior to seizures’ onset. These trends pointed to a slow decrease of the epileptic brain’s global complexity over time accompanied by an increase of the variance of complexity closer to seizure onsets. These results show that the global complexity of the epileptic brain decreases at least 1 h prior to seizures and imply that the employed methodology and measures could be useful in identifying different brain states, monitoring of seizure susceptibility over time, and potentially in seizure prediction. MDPI 2018-05-31 /pmc/articles/PMC7512937/ /pubmed/33265509 http://dx.doi.org/10.3390/e20060419 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Liu, Rui
Karumuri, Bharat
Adkinson, Joshua
Hutson, Timothy Noah
Vlachos, Ioannis
Iasemidis, Leon
Multivariate Matching Pursuit Decomposition and Normalized Gabor Entropy for Quantification of Preictal Trends in Epilepsy
title Multivariate Matching Pursuit Decomposition and Normalized Gabor Entropy for Quantification of Preictal Trends in Epilepsy
title_full Multivariate Matching Pursuit Decomposition and Normalized Gabor Entropy for Quantification of Preictal Trends in Epilepsy
title_fullStr Multivariate Matching Pursuit Decomposition and Normalized Gabor Entropy for Quantification of Preictal Trends in Epilepsy
title_full_unstemmed Multivariate Matching Pursuit Decomposition and Normalized Gabor Entropy for Quantification of Preictal Trends in Epilepsy
title_short Multivariate Matching Pursuit Decomposition and Normalized Gabor Entropy for Quantification of Preictal Trends in Epilepsy
title_sort multivariate matching pursuit decomposition and normalized gabor entropy for quantification of preictal trends in epilepsy
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7512937/
https://www.ncbi.nlm.nih.gov/pubmed/33265509
http://dx.doi.org/10.3390/e20060419
work_keys_str_mv AT liurui multivariatematchingpursuitdecompositionandnormalizedgaborentropyforquantificationofpreictaltrendsinepilepsy
AT karumuribharat multivariatematchingpursuitdecompositionandnormalizedgaborentropyforquantificationofpreictaltrendsinepilepsy
AT adkinsonjoshua multivariatematchingpursuitdecompositionandnormalizedgaborentropyforquantificationofpreictaltrendsinepilepsy
AT hutsontimothynoah multivariatematchingpursuitdecompositionandnormalizedgaborentropyforquantificationofpreictaltrendsinepilepsy
AT vlachosioannis multivariatematchingpursuitdecompositionandnormalizedgaborentropyforquantificationofpreictaltrendsinepilepsy
AT iasemidisleon multivariatematchingpursuitdecompositionandnormalizedgaborentropyforquantificationofpreictaltrendsinepilepsy