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spelling todo:paper_01650270_v153_n2_p163_Rosso2023-10-03T15:02:34Z EEG analysis using wavelet-based information tools Rosso, O.A. Martin, M.T. Figliola, A. Keller, K. Plastino, A. EEG Epileptic seizures Information theory Signal entropy Statistical complexity Wavelet analysis analytic method article electroencephalogram entropy epileptic discharge epileptic focus grand mal epilepsy human information processing information science priority journal scalp statistics tonic clonic seizure waveform adolescent adult brain child comparative study electroencephalography epilepsy female male pathophysiology physiology signal processing time Adolescent Adult Brain Child Electroencephalography Entropy Epilepsy Female Humans Male Signal Processing, Computer-Assisted Time Factors Wavelet-based informational tools for quantitative electroencephalogram (EEG) record analysis are reviewed. Relative wavelet energies, wavelet entropies and wavelet statistical complexities are used in the characterization of scalp EEG records corresponding to secondary generalized tonic-clonic epileptic seizures. In particular, we show that the epileptic recruitment rhythm observed during seizure development is well described in terms of the relative wavelet energies. In addition, during the concomitant time-period the entropy diminishes while complexity grows. This is construed as evidence supporting the conjecture that an epileptic focus, for this kind of seizures, triggers a self-organized brain state characterized by both order and maximal complexity. © 2006 Elsevier B.V. All rights reserved. Fil:Figliola, A. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_01650270_v153_n2_p163_Rosso
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic EEG
Epileptic seizures
Information theory
Signal entropy
Statistical complexity
Wavelet analysis
analytic method
article
electroencephalogram
entropy
epileptic discharge
epileptic focus
grand mal epilepsy
human
information processing
information science
priority journal
scalp
statistics
tonic clonic seizure
waveform
adolescent
adult
brain
child
comparative study
electroencephalography
epilepsy
female
male
pathophysiology
physiology
signal processing
time
Adolescent
Adult
Brain
Child
Electroencephalography
Entropy
Epilepsy
Female
Humans
Male
Signal Processing, Computer-Assisted
Time Factors
spellingShingle EEG
Epileptic seizures
Information theory
Signal entropy
Statistical complexity
Wavelet analysis
analytic method
article
electroencephalogram
entropy
epileptic discharge
epileptic focus
grand mal epilepsy
human
information processing
information science
priority journal
scalp
statistics
tonic clonic seizure
waveform
adolescent
adult
brain
child
comparative study
electroencephalography
epilepsy
female
male
pathophysiology
physiology
signal processing
time
Adolescent
Adult
Brain
Child
Electroencephalography
Entropy
Epilepsy
Female
Humans
Male
Signal Processing, Computer-Assisted
Time Factors
Rosso, O.A.
Martin, M.T.
Figliola, A.
Keller, K.
Plastino, A.
EEG analysis using wavelet-based information tools
topic_facet EEG
Epileptic seizures
Information theory
Signal entropy
Statistical complexity
Wavelet analysis
analytic method
article
electroencephalogram
entropy
epileptic discharge
epileptic focus
grand mal epilepsy
human
information processing
information science
priority journal
scalp
statistics
tonic clonic seizure
waveform
adolescent
adult
brain
child
comparative study
electroencephalography
epilepsy
female
male
pathophysiology
physiology
signal processing
time
Adolescent
Adult
Brain
Child
Electroencephalography
Entropy
Epilepsy
Female
Humans
Male
Signal Processing, Computer-Assisted
Time Factors
description Wavelet-based informational tools for quantitative electroencephalogram (EEG) record analysis are reviewed. Relative wavelet energies, wavelet entropies and wavelet statistical complexities are used in the characterization of scalp EEG records corresponding to secondary generalized tonic-clonic epileptic seizures. In particular, we show that the epileptic recruitment rhythm observed during seizure development is well described in terms of the relative wavelet energies. In addition, during the concomitant time-period the entropy diminishes while complexity grows. This is construed as evidence supporting the conjecture that an epileptic focus, for this kind of seizures, triggers a self-organized brain state characterized by both order and maximal complexity. © 2006 Elsevier B.V. All rights reserved.
format JOUR
author Rosso, O.A.
Martin, M.T.
Figliola, A.
Keller, K.
Plastino, A.
author_facet Rosso, O.A.
Martin, M.T.
Figliola, A.
Keller, K.
Plastino, A.
author_sort Rosso, O.A.
title EEG analysis using wavelet-based information tools
title_short EEG analysis using wavelet-based information tools
title_full EEG analysis using wavelet-based information tools
title_fullStr EEG analysis using wavelet-based information tools
title_full_unstemmed EEG analysis using wavelet-based information tools
title_sort eeg analysis using wavelet-based information tools
url http://hdl.handle.net/20.500.12110/paper_01650270_v153_n2_p163_Rosso
work_keys_str_mv AT rossooa eeganalysisusingwaveletbasedinformationtools
AT martinmt eeganalysisusingwaveletbasedinformationtools
AT figliolaa eeganalysisusingwaveletbasedinformationtools
AT kellerk eeganalysisusingwaveletbasedinformationtools
AT plastinoa eeganalysisusingwaveletbasedinformationtools
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