Multivariate symbol transfer entropy analysis on epileptic EEG

DOI

10.2991/icmse-15.2015.323How to use a DOI?

Keywords

Complexity theory, multivariate symbolic transfer entropy, EEG signal

Abstract

Epilepsy is caused by abnormal synchronous discharge of neurons in the brain, which is the main basis for the diagnosis of epilepsy. Use of complexity theory to study the epileptic signal has become a hot spot. The symbolic transfer entropy can be used as a characteristic of epilepsy playing an increasingly important role in the study of epilepsy in EEG feature extraction. But symbolic transfer entropy is generally used to measure the dynamic characteristics and directional information between two variables and ignores the interaction between multivariate. In this paper, epileptic EEG signals is analyzed based on multivariate symbol transfer entropy. By choosing the lead signal and the signal length and analyzing the robustness, the method can be used to distinguish between normal and patients with epilepsy. It is proved the algorithm is robust and reliable. The findings will help clinical diagnosis.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Qianqian Liu
AU  - Jun Wang
AU  - Fengzhen Hou
PY  - 2015/12
DA  - 2015/12
TI  - Multivariate symbol transfer entropy analysis on epileptic EEG
BT  - Proceedings of the 2015 6th International Conference on Manufacturing Science and Engineering
PB  - Atlantis Press
SP  - 1785
EP  - 1788
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmse-15.2015.323
DO  - 10.2991/icmse-15.2015.323
ID  - Liu2015/12
ER  -