Epileptic seizure detection in EEGs signals using a fast weighted horizontal visibility algorithm

Comput Methods Programs Biomed. 2014 Jul;115(2):64-75. doi: 10.1016/j.cmpb.2014.04.001. Epub 2014 Apr 15.

Authors

Affiliations

¹ Faculty of Health, Engineering and Sciences, University of Southern Queensland, Toowoomba, QLD 4350, Australia. Electronic address: Guohun.Zhu@usq.edu.au.
² Faculty of Health, Engineering and Sciences, University of Southern Queensland, Toowoomba, QLD 4350, Australia.

PMID: 24768081
DOI: 10.1016/j.cmpb.2014.04.001

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.

Keywords: Computational complexity; Epilepsy; Mean degree; Mean strength; Weighted horizontal visibility graph.

MeSH terms

Algorithms*
Computer Simulation
Databases, Factual / statistics & numerical data
Diagnosis, Computer-Assisted / methods*
Diagnosis, Computer-Assisted / statistics & numerical data
Electroencephalography / statistics & numerical data*
Epilepsy / diagnosis*
Fourier Analysis
Humans
Nonlinear Dynamics