Analysing epileptic EEGs with a visibility graph algorithm
Paper
Paper/Presentation Title | Analysing epileptic EEGs with a visibility graph algorithm |
---|---|
Presentation Type | Paper |
Authors | Zhu, Guohun (Author), Li, Yan (Author) and Wen, Peng (Paul) (Author) |
Editors | Chen, Qianbin, Huan, Jun (Luke), Xu, Yong, Zhang, Tianqi and Wang, Lipo |
Journal or Proceedings Title | Proceedings of the 5th International Conference on Biomedical Engineering and Informatics (BMEI 2012) |
ERA Conference ID | 60406 |
Number of Pages | 5 |
Year | 2012 |
Place of Publication | Piscataway, NJ. United States |
ISBN | 9781467311816 |
9781467311847 | |
Digital Object Identifier (DOI) | https://doi.org/10.1109/BMEI.2012.6513212 |
Web Address (URL) of Paper | http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6513212 |
Conference/Event | 5th International Conference on Biomedical Engineering and Informatics (BMEI 2012) |
International Conference on Biomedical Engineering and Informatics (BMEI) | |
Event Details | 5th International Conference on Biomedical Engineering and Informatics (BMEI 2012) Event Date 16 to end of 18 Oct 2012 Event Location Chongqing, China |
Event Details | International Conference on Biomedical Engineering and Informatics (BMEI) BMEI |
Abstract | This paper analyzes the human epileptic lectroencephalogram (EEG) based on a visibility graph algorithm. A single-channel EEG is mapped into a visibility graph (VG). Then its mean degree and degree distribution on the VG are extracted. It is shown that the mean degree on a VG from an epileptic subject is larger than that on a healthy subject based on the VG. The number of nodes having five degree on a VG from a healthy subject is significantly different from the number of nodes having the same degree on the VG from an epileptic subject. The mean degree and the number of nodes with five and eight degrees are used to discriminate the healthy EEGs, seizure EEGs and inter-ictal EEGs. Experimental results demonstrate that the visibility graph algorithm has a high classification accuracy to identify these three types of EEGs. |
Keywords | seizure; visibility graph; degree distribution; EEG; nonlinear discriminant analysis |
ANZSRC Field of Research 2020 | 400399. Biomedical engineering not elsewhere classified |
461399. Theory of computation not elsewhere classified | |
490404. Combinatorics and discrete mathematics (excl. physical combinatorics) | |
Public Notes | © 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Byline Affiliations | Department of Mathematics and Computing |
Centre for Systems Biology | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q194z/analysing-epileptic-eegs-with-a-visibility-graph-algorithm
Download files
1883
total views437
total downloads1
views this month1
downloads this month