Epileptic Detection Based On EEG Signal Using Graph Index Complexity & Average Weight Degree
DOI:
https://doi.org/10.32792/jeps.v12i1.147Abstract
EEG signal suffers from some problem which leads to difficult during diagnosis of diseases. This paper
is presented a new approach for diagnosing epilepsy disease through EEG signals are using Weighted
Visibility Graph(WVG) for build complex networks and then extracting two feature weight index
complexity and average weight index of the network which characterized by its ability to detect seizure
cases depending the amount of change in the EEG.
the suggested method has been tested with a publicly available benchmark database. So evaluation of
performance of proposed the method the experimental results with Support Vector Machine SVM
classifier gave us 100% accuracy for classifying healthy VS epileptic seizure signals and also when
testing every feature as separate way average have get 97% accuracy.
__________________________________________________________________________________
References
T. Musumeci, A. Bonaccorso, and G. Puglisi, “Epilepsy disease and nose-to-brain delivery of polymeric nanoparticles: An
overview,” Pharmaceutics, vol. 11, no. 3, 2019, doi: 10.3390/pharmaceutics11030118.
Supriya, Siuly, H. Wang, G. Zhuo, and Y. Zhang, “Analyzing EEG signal data for detection of epileptic seizure: Introducing weight
on visibility graph with complex network feature,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes
Bioinformatics), vol. 9877 LNCS, pp. 56–66, 2016, doi: 10.1007/978-3-319-46922-5_5.
I. Aliyu, Y. B. Lim, and C. G. Lim, “Epilepsy detection in EEG signal U sing recurrent neural network,” ACM Int. Conf.
Proceeding Ser., no. March, pp. 50–53, 2019, doi: 10.1145/3325773.3325785.
L. Wang et al., “Automatic epileptic seizure detection in EEG signals using multi-domain feature extraction and nonlinear
analysis,” Entropy, vol. 19, no. 6, pp. 1–17, 2017, doi: 10.3390/e19060222.
R. Buettner, J. Frick, and T. Rieg, “High-performance detection of epilepsy in seizure-free EEG recordings: A novel machine
learning approach using very specific epileptic EEG sub-bands,” 40th Int. Conf. Inf. Syst. ICIS 2019, pp. 1–16, 2020.
S. Beniczky and D. L. Schomer, “Electroencephalography: basic biophysical and technological aspects important for clinical
applications,” Epileptic Disord., vol. 22, no. 6, pp. 697–715, 2020, doi: 10.1684/epd.2020.1217.
N. Sadati, H. R. Mohseni, and A. Maghsoudi, “Epileptic seizure detection using neural fuzzy networks,” IEEE Int. Conf. Fuzzy
Syst., no. January, pp. 596–600, 2006, doi: 10.1109/FUZZY.2006.1681772.
A. Shoka, M. Dessouky, A. El-Sherbeny, and A. El-Sayed, “Literature Review on EEG Preprocessing, Feature Extraction, and
Classifications Techniques,” Menoufia J. Electron. Eng. Res., vol. 28, no. 1, pp. 292–299, 2019, doi: 10.21608/mjeer.2019.64927.
A. Snarskii and I. Bezsudnov, “Critical phenomena in the dynamical visibility graph,” ArXiv e-prints, 2013.
A. A. Snarskii and I. V. Bezsudnov, “Phase transition in the parametric natural visibility graph,” Phys. Rev. E, vol. 94, no. 4, pp. 1–
, 2016, doi: 10.1103/PhysRevE.94.042137.
X. Lan, H. Mo, S. Chen, Q. Liu, and Y. Deng, “Fast transformation from time series to visibility graphs,” Chaos, vol. 25, no. 8, p.
, 2015, doi: 10.1063/1.4927835.
S. Supriya, S. Siuly, H. Wang, J. Cao, and Y. Zhang, “Weighted Visibility Graph with Complex Network Features in the Detection
of Epilepsy,” IEEE Access, vol. 4, pp. 6554–6566, 2016, doi: 10.1109/ACCESS.2016.2612242.
H. Yu et al., “Identification of Alzheimer‟s EEG With a WVG Network-Based Fuzzy Learning Approach,” Front. Neurosci., vol.
, no. July, pp. 1–15, 2020, doi: 10.3389/fnins.2020.00641.
X. Tang, L. Xia, Y. Liao, W. Liu, and Y. Peng, “New Approach to Epileptic Diagnosis Using Visibility Graph of High-Frequency
Signal,” vol. 44, no. 2, pp. 150–156, 2013, doi: 10.1177/1550059412464449.
R. G. Andrzejak, K. Lehnertz, F. Mormann, C. Rieke, P. David, and C. E. Elger, “Indications of nonlinear deterministic and finitedimensional
structures in time series of brain electrical activity: Dependence on recording region and brain state,” Phys. Rev. E -
Stat. Physics, Plasmas, Fluids, Relat. Interdiscip. Top., vol. 64, no. 6, p. 8, 2001, doi: 10.1103/PhysRevE.64.061907.
A. Ahmadi, V. Shalchyan, and M. R. Daliri, “A new method for epileptic seizure classification in EEG using adapted wavelet
packets,” 2017 Electr. Electron. Comput. Sci. Biomed. Eng. Meet. EBBT 2017, 2017, doi: 10.1109/EBBT.2017.7956756.
D. Fano Yela, F. Thalmann, V. Nicosia, D. Stowell, and M. Sandler, “Online visibility graphs: Encoding visibility in a binary search
tree,” Phys. Rev. Res., vol. 2, no. 2, p. 23069, 2020, doi: 10.1103/physrevresearch.2.023069.
Y. Zou, R. V. Donner, N. Marwan, J. F. Donges, and J. Kurths, “Complex network approaches to nonlinear time series analysis,”
Phys. Rep., vol. 787, pp. 1–97, 2019, doi: 10.1016/j.physrep.2018.10.005.
S. Supriya, S. Siuly, and Y. Zhang, “Automatic epilepsy detection from EEG introducing a new edge weight method in the complex
network,” Electron. Lett., vol. 52, no. 17, pp. 1430–1432, 2016, doi: 10.1049/el.2016.1992.
M. Ahmadlou, A. Adeli, R. Bajo, and H. Adeli, “Complexity of functional connectivity networks in mild cognitive impairment
subjects during a working memory task,” Clin. Neurophysiol., vol. 125, no. 4, pp. 694–702, 2014, doi:
1016/j.clinph.2013.08.033.
Z. Zhang, Y. Qin, L. Jia, and X. Chen, “Visibility graph feature model of vibration signals: A novel bearing fault diagnosis
approach,” Materials (Basel)., vol. 11, no. 11, pp. 1–16, 2018, doi: 10.3390/ma11112262.
S. Student, J. Pieter, and K. Fujarewicz, “Multiclass Classification Problem of Large-Scale Biomedical Meta-Data,” Procedia
Technol., vol. 22, pp. 938–945, 2016, doi: 10.1016/j.protcy.2016.01.093.
K. Saravananathan and T. Velmurugan, “Analyzing Diabetic Data using Classification Algorithms in Data Mining,” Indian J. Sci.
Technol., vol. 9, no. 43, 2016, doi: 10.17485/ijst/2016/v9i43/93874.
G. Zhu, Y. Li, and P. P. Wen, “Epileptic seizure detection in EEGs signals using a fast weighted horizontal visibility algorithm,”
Comput. Methods Programs Biomed., vol. 115, no. 2, pp. 64–75, 2014, doi: 10.1016/j.cmpb.2014.04.001.
Downloads
Published
Issue
Section
License
Copyright Policy
Authors retain copyright of their articles published in the Journal of Education for Pure Science (JEPS).
By submitting their work, authors grant the journal a non-exclusive license to publish, distribute, and archive the article in all formats and media.
License
All articles published in JEPS are licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license permits unrestricted use, distribution, and reproduction in any medium, provided that the original author(s) and the source are properly credited.
Author Rights
Authors have the right to:
-
Share their articles on personal websites, institutional repositories, and academic platforms
-
Reuse their work in future research and publications
-
Distribute the published version without restriction
Journal Rights
The journal retains the right to:
-
Publish and archive the articles
-
Include them in indexing and archiving systems such as LOCKSS and CLOCKSS
-
Promote and disseminate the published work
Responsibility
The contents of all articles are the sole responsibility of the authors. The journal, editors, and editorial board are not responsible for any errors, opinions, or statements expressed in the published articles.
Open Access Statement
JEPS provides immediate open access to its content, supporting the principle that making research freely available to the public enhances global knowledge exchange.
This work is licensed under a Creative Commons Attribution 4.0 International License.
https://creativecommons.org/licenses/by/4.0/
