Multiscale Relative Transition Entropy Analysis of Electroencephalogram

DOI

10.2991/ameii-16.2016.125How to use a DOI?

Keywords

multiscale, relative transition entropy, complexity.

Abstract

Recently, quantifying the complexity of the physiological time series has become more and more concerned. As one of the most complex physiological signals, electroencephalogram (EEG) including a large number of physiological and pathological information attracts widespread interest. However, many traditional algorithms fail to account for the multiple times scales inherent in physiologic dynamics. In this paper, we proposed multiscale relative transition entropy algorithm (MRTE) to analyze the white noise and pink noise, the adolescent and adults EEG as well as normal and epileptic EEG. The results indicate that there are distinct tendency among different types EEG which indicating that the multiscale relative transition entropy can distinguish different physiological and pathological signals.

Copyright

© 2016, 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  - Ying Wang
AU  - Fengzhen Hou
AU  - Jiafei Dai
AU  - Jin Li
AU  - Jun Wang
PY  - 2016/04
DA  - 2016/04
TI  - Multiscale Relative Transition Entropy Analysis of Electroencephalogram
BT  - Proceedings of the 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2016)
PB  - Atlantis Press
SP  - 628
EP  - 632
SN  - 2352-5401
UR  - https://doi.org/10.2991/ameii-16.2016.125
DO  - 10.2991/ameii-16.2016.125
ID  - Wang2016/04
ER  -