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Cited by 216EEG and Brain-Computer InterfacesComplex Systems and Time Series AnalysisTime Series Analysis and Forecasting

Weighted visibility graph with complex network features in the detection of epilepsy

Open access
Supriya, Supriya, Siuly, Siuly, Wang, Hua, Cao, Jinli, Zhang, Yanchun·2016·IEEE Access
DOI 10.1109/access.2016.2612242
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Abstract

Epilepsy detection from electrical characteristics of EEG signals obtained from the brain of undergone subject is a challenge task for both research and neurologist due to the non-stationary and chaotic nature of EEG signals. As epileptic EEG signals contain huge fluctuating information about the functional behavior of the brain, it is hard to distinguish the fundamental dynamic, complex network of EEG signals without considering the strength among the nodes as they are connected with each other on the basis of these strengths. The prior research on natural visibility graph did not consider this issue in epileptic seizure, although it is a very important key point to have representative information from the signals. Hence, this paper aims to introduce a new idea for epilepsy detection using complex network statistical properties by measuring different strengths of the edges in natural visibility graph theory, which is considered as weight. Thus, the proposed method is named “weighted visibility graph”. In this proposed method, first, the epileptic EEG signals are transformed into complex network and then two important statistical properties of a network such as modularity and average weighted degree used for extracting the imperative characteristics from a network of EEG signals. After that, the extracted features are evaluated by two modern machine-learning classifiers such as, support vector machine with a different kernel function and k-nearest neighbor. The experimental results demonstrate that the combined effect of both features is valuable for network metrics to characterize the EEG time series signals in case of weighted complex network generating up to 100% classification accuracy.

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  • Most studies using complex networks as preprocessing convert raw time series into graphs, extract topological statistics, and feed them into DNN models.

    “In particular, most of the studies in this area use complex networks to convert raw time series data into graphs and then extract the topological statistics (such as cluster coefficients, path lengths, and node edge distributions) from the graphs and feed them into the DNN models.”

    From TVGeAN: Tensor Visibility Graph-Enhanced Attention Network for Versatile Multivariant Time Series Learning Tasks (2024) · cites this paper as [28]

    The paper’s opening summary describes converting EEG time series into a complex network and extracting network statistics, including modularity and average weighted degree. But it feeds those features into SVM and KNN classifiers, not DNN models. The paper also does not establish that most studies use the claimed pipeline. (LLM, conf=0.90)

    Experimental — accuracy not yet measured

  • Edge weights in TVG are defined based on the visibility angle algorithm.

    “the weights of the edges are defined based on the visibility angle algorithm [28].”

    From TVGeAN: Tensor Visibility Graph-Enhanced Attention Network for Versatile Multivariant Time Series Learning Tasks (2024) · cites this paper as [28]

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Research works printed but not linked: 34

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  34. [36]Y. Zhang, L. Sun, H. Song, and X. Cao, ‘‘Ubiquitous WSN for healthcare: Recent advances and future prospects,’’ IEEE Internet Things J., vol. 1, no. 4, pp. 311–318, 2014. SUPRIYA SUPRIYA received the B.Eng. degree in information technology and the M.Eng. degree in computer science from Punjab Technical Uni-versity, Jalandhar, India. She is currently pursu-ing the Ph.D. degree with the Centre for Applied Informatics, College of Engineering and Science, Victoria University, Australia. Her research inter-ests are biomedical signal processing, signal clas-sification, detection and prediction of epileptic seizure, pattern recognition, artificial intelligence, deep Web, data mining, and Web mining. SIULY SIULY received the B.Sc. and M.Sc. degrees in statistics from the University of Dhaka, Bangladesh, and the Ph.D. degree in biomedi-cal engineering from the University of Southern Queensland, Australia, in 2012. She is a Research Fellow with the Centre for Applied Informat-ics, College of Engineering and Science, Victo-ria University, Australia. Her research interests are biomedical signal processing, signal classifica-tion, detection and prediction of epileptic seizure, brain–computer interface, machine learning, pattern recognition, and artifi-cial intelligence. HUA WANG received the Ph.D. degree in com-puter science from the University of Southern Queensland (USQ) in 2004. He was a Professor with USQ from 2011 to 2013. He is currently a Full-Time Professor with the Centre for Applied Informatics, Victoria University. He has authored or co-authored over 150 peer-reviewed research papers mainly in data security, data mining, access control, privacy and Web services, as well as their applications in the fields of e-health and e-environment. JINLI CAO received the Ph.D. degree in computer science from the University of Southern Queens-land, Australia, in 1997. He is currently an Active Researcher, where he is involved in database sys-tems, such as XML queries, keyword search, top-k queries in probabilistic databases, recom-mendation systems, decision support, and cloud computing. He has authored or co-authored over 90 research papers in international conferences and journals, such as the IEEE TRANSACTIONS on DISTRIBUTED and PARALLEL PROCESSING, the IEEE TRANSACTIONS on KNOWLEDGE and DATA ENGINEERING, the Journal of Computer and System Sciences, and the Computer Journal, and top conferences, such as WWW, WISE, and DASFAA. YANCHUN ZHANG is currently the Director of the Centre for Applied Informatics, and coor-dinates a multidisciplinary e-research program across the Victoria University. He is also an International Research Leader in databases, data mining, health informatics, Web information sys-tems, and Web services. He has authored over 220 research papers in international journals and conferences proceedings, and authored/edited 12 books. His major research interests include data mining, pattern recognition, machine learning, biomedical signal processing, databases, data management, e-health, environmental studies, and sensor networks. He is the Editor-in-Chief of World Wide Web Journal (Springer), and the Health Information Science and Systems. He is also the Chairman of the International Web Information Systems Engineering Society. 6566 VOLUME 4, 2016

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Authors
Supriya, S., Siuly, S., Wang, H., Cao, J., & Zhang, Y.
Affiliations
  • Victoria University (AU)
  • La Trobe University (AU)
ORCID
Venue
IEEE Access
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2016
Volume
4
Pages
6554-6566
ISSN
2169-3536 (ISSN-L)
DOI
10.1109/access.2016.2612242
OpenAlex
W2521472744
Cited by
216
References
1
Citations by year
2026: 16 · 2025: 15 · 2024: 22 · 2023: 26 · 2022: 23 · 2021: 26 · 2020: 34 · 2019: 24 · 2018: 23 · 2017: 7
FWCI
11.48
Type
journal article
Language
en
Open access
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/OAPA.html (vor)
Locations
  • IEEE Access (publishedVersion)
  • Victoria University Research Repository (Victoria University) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Complex Systems and Time Series Analysis
  • Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
Keywords
seizure detection, EEG signals, complex networks, network modularity, epileptic seizure classification, network metrics, support vector machine, k-nearest neighbors

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