IEICE TRANSACTIONS on Information
Adaptive Subscale Entropy Based Quantification of EEG
Summary :
This letter presents a new entropy measure for electroencephalograms (EEGs), which reflects the underlying dynamics of EEG over multiple time scales. The motivation behind this study is that neurological signals such as EEG possess distinct dynamics over different spectral modes. To deal with the nonlinear and nonstationary nature of EEG, the recently developed empirical mode decomposition (EMD) is incorporated, allowing an EEG to be decomposed into its inherent spectral components, referred to as intrinsic mode functions (IMFs). By calculating Shannon entropy of IMFs in a time-dependent manner and summing them over adaptive multiple scales, the result is an adaptive subscale entropy measure of EEG. Simulation and experimental results show that the proposed entropy properly reveals the dynamical changes over multiple scales.
- Publication
- IEICE TRANSACTIONS on Information Vol.E97-D No.5 pp.1398-1401
- Publication Date
- 2014/05/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E97.D.1398
- Type of Manuscript
- LETTER
- Category
- Biological Engineering
Authors
Young-Seok CHOI
Gangneung Wonju National University
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Young-Seok CHOI, "Adaptive Subscale Entropy Based Quantification of EEG" in IEICE TRANSACTIONS on Information,
vol. E97-D, no. 5, pp. 1398-1401, May 2014, doi: 10.1587/transinf.E97.D.1398.
Abstract: This letter presents a new entropy measure for electroencephalograms (EEGs), which reflects the underlying dynamics of EEG over multiple time scales. The motivation behind this study is that neurological signals such as EEG possess distinct dynamics over different spectral modes. To deal with the nonlinear and nonstationary nature of EEG, the recently developed empirical mode decomposition (EMD) is incorporated, allowing an EEG to be decomposed into its inherent spectral components, referred to as intrinsic mode functions (IMFs). By calculating Shannon entropy of IMFs in a time-dependent manner and summing them over adaptive multiple scales, the result is an adaptive subscale entropy measure of EEG. Simulation and experimental results show that the proposed entropy properly reveals the dynamical changes over multiple scales.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E97.D.1398/_p
Copy
@ARTICLE{e97-d_5_1398,
author={Young-Seok CHOI, },
journal={IEICE TRANSACTIONS on Information},
title={Adaptive Subscale Entropy Based Quantification of EEG},
year={2014},
volume={E97-D},
number={5},
pages={1398-1401},
abstract={This letter presents a new entropy measure for electroencephalograms (EEGs), which reflects the underlying dynamics of EEG over multiple time scales. The motivation behind this study is that neurological signals such as EEG possess distinct dynamics over different spectral modes. To deal with the nonlinear and nonstationary nature of EEG, the recently developed empirical mode decomposition (EMD) is incorporated, allowing an EEG to be decomposed into its inherent spectral components, referred to as intrinsic mode functions (IMFs). By calculating Shannon entropy of IMFs in a time-dependent manner and summing them over adaptive multiple scales, the result is an adaptive subscale entropy measure of EEG. Simulation and experimental results show that the proposed entropy properly reveals the dynamical changes over multiple scales.},
keywords={},
doi={10.1587/transinf.E97.D.1398},
ISSN={1745-1361},
month={May},}
Copy
TY - JOUR
TI - Adaptive Subscale Entropy Based Quantification of EEG
T2 - IEICE TRANSACTIONS on Information
SP - 1398
EP - 1401
AU - Young-Seok CHOI
PY - 2014
DO - 10.1587/transinf.E97.D.1398
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E97-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2014
AB - This letter presents a new entropy measure for electroencephalograms (EEGs), which reflects the underlying dynamics of EEG over multiple time scales. The motivation behind this study is that neurological signals such as EEG possess distinct dynamics over different spectral modes. To deal with the nonlinear and nonstationary nature of EEG, the recently developed empirical mode decomposition (EMD) is incorporated, allowing an EEG to be decomposed into its inherent spectral components, referred to as intrinsic mode functions (IMFs). By calculating Shannon entropy of IMFs in a time-dependent manner and summing them over adaptive multiple scales, the result is an adaptive subscale entropy measure of EEG. Simulation and experimental results show that the proposed entropy properly reveals the dynamical changes over multiple scales.
ER -
English
