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2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery
Auto Mutual Information Analysis with Order Patterns for Epileptic EEG
Tianjin, China
August 14-August 16
ISBN: 978-0-7695-3735-1
ASCII Text
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Gaoxiang Ouyang, Yao Wang, Xiaoli Li, "Auto Mutual Information Analysis with Order Patterns for Epileptic EEG," Fuzzy Systems and Knowledge Discovery, Fourth International Conference on, vol. 5, pp. 23-27, 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery, 2009.
 
 
BibTex
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@article{ 10.1109/FSKD.2009.33,
author = {Gaoxiang Ouyang and Yao Wang and Xiaoli Li},
title = {Auto Mutual Information Analysis with Order Patterns for Epileptic EEG},
journal ={Fuzzy Systems and Knowledge Discovery, Fourth International Conference on},
volume = {5},
year = {2009},
isbn = {978-0-7695-3735-1},
pages = {23-27},
doi = {http://doi.ieeecomputersociety.org/10.1109/FSKD.2009.33},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Fuzzy Systems and Knowledge Discovery, Fourth International Conference on
TI - Auto Mutual Information Analysis with Order Patterns for Epileptic EEG
SN - 978-0-7695-3735-1
SP23
EP27
A1 - Gaoxiang Ouyang,
A1 - Yao Wang,
A1 - Xiaoli Li,
PY - 2009
KW - auto mutual information
KW - order patterns
KW - epileptic EEG
KW - classification
VL - 5
JA - Fuzzy Systems and Knowledge Discovery, Fourth International Conference on
ER -
 
In this study, we investigated auto mutual information (AMI), based on order patterns analysis, as a tool to evaluate the dynamical characteristics of electroencephalogram (EEG) during interictal, preictal and ictal phase, respectively. Permutation entropy quantifies regularity in time series, while AMI detects the mutual information (MI) between a time series and a delayed version of itself. The results show that AMI method was able to reveal that the highest entropy values were assigned to interictal EEG recordings and the lowest entropy values were assigned to ictal EEG recordings. The classification ability of the AMI measures is tested using ANFIS classifier. Test results confirm that AMI method has potential in classifying the epileptic EEG signals.
Index Terms:
auto mutual information, order patterns, epileptic EEG, classification
Citation:
Gaoxiang Ouyang, Yao Wang, Xiaoli Li, "Auto Mutual Information Analysis with Order Patterns for Epileptic EEG," fskd, vol. 5, pp.23-27, 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery, 2009
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