Information-Theoretical Quantifier of Brain Rhythm Based on Data-Driven Multiscale Representation
This paper presents a data-driven multiscale entropy measure to reveal the scale dependent information quantity of electroencephalogram (EEG) recordings. This work is motivated by the previous observations on the nonlinear and nonstationary nature of EEG over multiple time scales. Here, a new framew...
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doaj-77031bf0dff241b6bdf2c68c8eb361f82020-11-24T23:52:18ZengHindawi LimitedBioMed Research International2314-61332314-61412015-01-01201510.1155/2015/830926830926Information-Theoretical Quantifier of Brain Rhythm Based on Data-Driven Multiscale RepresentationYoung-Seok Choi0Department of Electronic Engineering, Gangneung-Wonju National University, Gangneung 210-702, Republic of KoreaThis paper presents a data-driven multiscale entropy measure to reveal the scale dependent information quantity of electroencephalogram (EEG) recordings. This work is motivated by the previous observations on the nonlinear and nonstationary nature of EEG over multiple time scales. Here, a new framework of entropy measures considering changing dynamics over multiple oscillatory scales is presented. First, to deal with nonstationarity over multiple scales, EEG recording is decomposed by applying the empirical mode decomposition (EMD) which is known to be effective for extracting the constituent narrowband components without a predetermined basis. Following calculation of Renyi entropy of the probability distributions of the intrinsic mode functions extracted by EMD leads to a data-driven multiscale Renyi entropy. To validate the performance of the proposed entropy measure, actual EEG recordings from rats n=9 experiencing 7 min cardiac arrest followed by resuscitation were analyzed. Simulation and experimental results demonstrate that the use of the multiscale Renyi entropy leads to better discriminative capability of the injury levels and improved correlations with the neurological deficit evaluation after 72 hours after cardiac arrest, thus suggesting an effective diagnostic and prognostic tool.http://dx.doi.org/10.1155/2015/830926 |
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DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Young-Seok Choi |
spellingShingle |
Young-Seok Choi Information-Theoretical Quantifier of Brain Rhythm Based on Data-Driven Multiscale Representation BioMed Research International |
author_facet |
Young-Seok Choi |
author_sort |
Young-Seok Choi |
title |
Information-Theoretical Quantifier of Brain Rhythm Based on Data-Driven Multiscale Representation |
title_short |
Information-Theoretical Quantifier of Brain Rhythm Based on Data-Driven Multiscale Representation |
title_full |
Information-Theoretical Quantifier of Brain Rhythm Based on Data-Driven Multiscale Representation |
title_fullStr |
Information-Theoretical Quantifier of Brain Rhythm Based on Data-Driven Multiscale Representation |
title_full_unstemmed |
Information-Theoretical Quantifier of Brain Rhythm Based on Data-Driven Multiscale Representation |
title_sort |
information-theoretical quantifier of brain rhythm based on data-driven multiscale representation |
publisher |
Hindawi Limited |
series |
BioMed Research International |
issn |
2314-6133 2314-6141 |
publishDate |
2015-01-01 |
description |
This paper presents a data-driven multiscale entropy measure to reveal the scale dependent information quantity of electroencephalogram (EEG) recordings. This work is motivated by the previous observations on the nonlinear and nonstationary nature of EEG over multiple time scales. Here, a new framework of entropy measures considering changing dynamics over multiple oscillatory scales is presented. First, to deal with nonstationarity over multiple scales, EEG recording is decomposed by applying the empirical mode decomposition (EMD) which is known to be effective for extracting the constituent narrowband components without a predetermined basis. Following calculation of Renyi entropy of the probability distributions of the intrinsic mode functions extracted by EMD leads to a data-driven multiscale Renyi entropy. To validate the performance of the proposed entropy measure, actual EEG recordings from rats n=9 experiencing 7 min cardiac arrest followed by resuscitation were analyzed. Simulation and experimental results demonstrate that the use of the multiscale Renyi entropy leads to better discriminative capability of the injury levels and improved correlations with the neurological deficit evaluation after 72 hours after cardiac arrest, thus suggesting an effective diagnostic and prognostic tool. |
url |
http://dx.doi.org/10.1155/2015/830926 |
work_keys_str_mv |
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