Zhu, Guohun and Li, Yan 
ORCID: https://orcid.org/0000-0002-4694-4926 and Wen, Peng (Paul) ORCID: https://orcid.org/0000-0003-0939-9145
(2014)
Epileptic seizure detection in EEGs signals using a fast weighted horizontal visibility algorithm.
Computer Methods and Programs in Biomedicine, 115 (2).
pp. 64-75.
ISSN 0169-2607
Abstract
This paper proposes a fast weighted horizontal visibility graph constructing algorithm (FWHVA) to identify seizure from EEG signals. The performance of the FWHVA is evaluated by comparing with Fast Fourier Transform (FFT) and sample entropy (SampEn) method. Two noise-robustness graph features based on the FWHVA, mean degree and mean strength, are investigated using two chaos signals and five groups of EEG signals. Experimental results show that feature extraction using the FWHVA is faster than that of SampEn and FFT. And mean strength feature associated with ictal EEG is significant higher than that of healthy and inter-ictal EEGs. In addition, an 100% classification accuracy for identifying seizure from healthy shows that the features based on the FWHVA are more promising than the frequency features based on FFT and entropy indices based on SampEn for time series classification.
|
Statistics for this ePrint Item |
| Item Type: | Article (Commonwealth Reporting Category C) |
|---|---|
| Refereed: | Yes |
| Item Status: | Live Archive |
| Additional Information: | Files associated with this item cannot be displayed due to copyright restrictions. |
| Faculty/School / Institute/Centre: | Historic - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences (1 Jul 2013 - 5 Sep 2019) |
| Faculty/School / Institute/Centre: | Historic - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences (1 Jul 2013 - 5 Sep 2019) |
| Date Deposited: | 21 Apr 2015 04:00 |
| Last Modified: | 04 Jul 2016 05:14 |
| Uncontrolled Keywords: | epilepsy; computational complexity; weighted horizontal visibility graph; mean degree; mean strength |
| Fields of Research (2008): | 09 Engineering > 0903 Biomedical Engineering > 090399 Biomedical Engineering not elsewhere classified 01 Mathematical Sciences > 0102 Applied Mathematics > 010202 Biological Mathematics 08 Information and Computing Sciences > 0802 Computation Theory and Mathematics > 080201 Analysis of Algorithms and Complexity |
| Fields of Research (2020): | 40 ENGINEERING > 4003 Biomedical engineering > 400399 Biomedical engineering not elsewhere classified 49 MATHEMATICAL SCIENCES > 4901 Applied mathematics > 490102 Biological mathematics 46 INFORMATION AND COMPUTING SCIENCES > 4613 Theory of computation > 461399 Theory of computation not elsewhere classified |
| Socio-Economic Objectives (2008): | C Society > 92 Health > 9202 Health and Support Services > 920203 Diagnostic Methods |
| Identification Number or DOI: | https://doi.org/10.1016/j.cmpb.2014.04.001 |
| URI: | http://eprints.usq.edu.au/id/eprint/25346 |
Actions (login required)
 |
Archive Repository Staff Only |