Analyzing EEG of Quasi-Brain-Death Based on Dynamic Sample Entropy Measures

Analyzing EEG of Quasi-Brain-Death Based on Dynamic Sample Entropy Measures

To give a more definite criterion using electroencephalograph (EEG) approach on brain death determination is vital for both reducing the risks and preventing medical misdiagnosis. This paper presents several novel adaptive computable entropy methods based on approximate entropy (ApEn) and sample ent...

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Main Authors: Ni, Li, Cao, Jianting, Wang, Rubin
Format: Online
Language:English
Published: Hindawi Publishing Corporation 2013
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3881453/
id pubmed-3881453
recordtype oai_dc
spelling pubmed-38814532014-01-20 Analyzing EEG of Quasi-Brain-Death Based on Dynamic Sample Entropy Measures Ni, Li Cao, Jianting Wang, Rubin Research Article To give a more definite criterion using electroencephalograph (EEG) approach on brain death determination is vital for both reducing the risks and preventing medical misdiagnosis. This paper presents several novel adaptive computable entropy methods based on approximate entropy (ApEn) and sample entropy (SampEn) to monitor the varying symptoms of patients and to determine the brain death. The proposed method is a dynamic extension of the standard ApEn and SampEn by introducing a shifted time window. The main advantages of the developed dynamic approximate entropy (DApEn) and dynamic sample entropy (DSampEn) are for real-time computation and practical use. Results from the analysis of 35 patients (63 recordings) show that the proposed methods can illustrate effectiveness and well performance in evaluating the brain consciousness states. Hindawi Publishing Corporation 2013 2013-12-22 /pmc/articles/PMC3881453/ /pubmed/24454537 http://dx.doi.org/10.1155/2013/618743 Text en Copyright © 2013 Li Ni et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
repository_type Open Access Journal
institution_category Foreign Institution
institution US National Center for Biotechnology Information
building NCBI PubMed
collection Online Access
language English
format Online
author Ni, Li
Cao, Jianting
Wang, Rubin
spellingShingle Ni, Li
Cao, Jianting
Wang, Rubin
Analyzing EEG of Quasi-Brain-Death Based on Dynamic Sample Entropy Measures
author_facet Ni, Li
Cao, Jianting
Wang, Rubin
author_sort Ni, Li
title Analyzing EEG of Quasi-Brain-Death Based on Dynamic Sample Entropy Measures
title_short Analyzing EEG of Quasi-Brain-Death Based on Dynamic Sample Entropy Measures
title_full Analyzing EEG of Quasi-Brain-Death Based on Dynamic Sample Entropy Measures
title_fullStr Analyzing EEG of Quasi-Brain-Death Based on Dynamic Sample Entropy Measures
title_full_unstemmed Analyzing EEG of Quasi-Brain-Death Based on Dynamic Sample Entropy Measures
title_sort analyzing eeg of quasi-brain-death based on dynamic sample entropy measures
description To give a more definite criterion using electroencephalograph (EEG) approach on brain death determination is vital for both reducing the risks and preventing medical misdiagnosis. This paper presents several novel adaptive computable entropy methods based on approximate entropy (ApEn) and sample entropy (SampEn) to monitor the varying symptoms of patients and to determine the brain death. The proposed method is a dynamic extension of the standard ApEn and SampEn by introducing a shifted time window. The main advantages of the developed dynamic approximate entropy (DApEn) and dynamic sample entropy (DSampEn) are for real-time computation and practical use. Results from the analysis of 35 patients (63 recordings) show that the proposed methods can illustrate effectiveness and well performance in evaluating the brain consciousness states.
publisher Hindawi Publishing Corporation
publishDate 2013
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3881453/
_version_ 1612044462852145152

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