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Detection of EEG signals in normal and epileptic seizures with multiscale multifractal analysis approach via weighted horizontal visibility graph |
| Lu Ma(马璐)1,2, Yan-Lin Ren(任彦霖)3, Ai-Jun He(何爱军)4, De-Qiang Cheng(程德强)1, and Xiao-Dong Yang(杨小冬)3,† |
1 School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China;
2 Suzhou Vocational and Technical College, Suzhou 234000, China;
3 School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China;
4 School of Electronic Science and Engineering, Nanjing University, Nanjing 210023, China |
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Abstract Electroencephalogram (EEG) signals contain important information about the regulation of brain system. Thus, automatic detection of epilepsy by analyzing the characteristics obtained from EEG signals has important research implications in the field of clinical medicine. In this paper, the horizontal visibility graph (HVG) algorithm is used to map multifractal EEG signals into complex networks. Then, we study the structure of the networks and explore the nonlinear dynamics properties of the EEG signals inherited from these networks. In order to better describe complex brain behaviors, we use the angle between two connected nodes as the edge weight of the network and construct the weighted horizontal visibility graph (WHVG). In our studies, fractality and multifractality of WHVG are innovatively used to analyze the structure of related networks. However, these methods only analyze the reconstructed dynamical system in general characterizations, they are not sufficient to describe the complex behavior and cannot provide a comprehensive picture of the system. To this effect, we propose an improved multiscale multifractal analysis (MMA) for network, which extends the description of the network dynamics features by focusing on the relationship between the multifractality and the measured scale-free intervals. Furthermore, neural networks are applied to train the above-mentioned parameters for the classification and identification of three kinds of EEG signals, i.e., health, interictal phase, and ictal phase. By evaluating our experimental results, the classification accuracy is 99.0%, reflecting the effectiveness of the WHVG algorithm in extracting the potential dynamic characteristics of EEG signals.
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Received: 17 January 2023
Revised: 07 June 2023
Accepted manuscript online: 20 June 2023
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PACS:
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05.10.-a
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(Computational methods in statistical physics and nonlinear dynamics)
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87.18.-h
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(Biological complexity)
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87.85.Ng
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(Biological signal processing)
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87.19.X-
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(Diseases)
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| Fund: Project supported by the Xuzhou Key Research and Development Program (Social Development) (Grant No. KC21304) and the National Natural Science Foundation of China (Grant No. 61876186). |
Corresponding Authors:
Xiao-Dong Yang
E-mail: xyang@cumt.edu.cn
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Cite this article:
Lu Ma(马璐), Yan-Lin Ren(任彦霖), Ai-Jun He(何爱军), De-Qiang Cheng(程德强), and Xiao-Dong Yang(杨小冬) Detection of EEG signals in normal and epileptic seizures with multiscale multifractal analysis approach via weighted horizontal visibility graph 2023 Chin. Phys. B 32 110506
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