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Recurrence Network Analysis of the Synchronous EEG Time Series in Normal and Epileptic Brains

  • Translational Biomedical Research
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Abstract

We sought to analyze the dynamic properties of brain electrical activity from healthy volunteers and epilepsy patients using recurrence networks. Phase-space trajectories of synchronous electroencephalogram signals were obtained through embedding dimension in phase-space reconstruction based on the distance set of space points. The recurrence matrix calculated from phase-space trajectories was identified with the adjacency matrix of a complex network. Then, we applied measures to characterize the complex network to this recurrence network. A detailed analysis revealed the following: (1) The recurrence networks of normal brains exhibited a sparser connectivity and smaller clustering coefficient compared with that of epileptic brains; (2) the small-world property existed in both normal and epileptic brains consistent with the previous empirical studies of structural and functional brain networks; and (3) the assortative property of the recurrence network was found by computing the assortative coefficients; their values increased from normal to epileptic brain which accurately suggested the difference of the states. These universal and non-universal characteristics of recurrence networks might help clearly understand the underlying neurodynamics of the brain and provide an efficient tool for clinical diagnosis.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China under Grant Nos. 60874090, 60974079, 61004102. S-MC appreciates the financial support from the Chinese Postdoctoral Science Foundation (No.20100480704) and the fundamental research funds for the Central Universities (No.WK2100230004).

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Authors and Affiliations

  1. Department of Neurology, The Affiliated Jiangyin Hospital of Southeast University of Medical College, Jiangyin, 214400, Jiangsu, China

    Peng Lang & Dong-Bai Liu

  2. Department of Electronic Science and Technology, University of Science and Technology of China, Hefei, 230026, Anhui, China

    Shi-Min Cai, Lei Hong & Pei-Ling Zhou

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  1. Peng Lang
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  2. Dong-Bai Liu
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  3. Shi-Min Cai
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  4. Lei Hong
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  5. Pei-Ling Zhou
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Correspondence to Dong-Bai Liu.

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Lang, P., Liu, DB., Cai, SM. et al. Recurrence Network Analysis of the Synchronous EEG Time Series in Normal and Epileptic Brains. Cell Biochem Biophys 66, 331–336 (2013). https://doi.org/10.1007/s12013-012-9452-0

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  • DOI: https://doi.org/10.1007/s12013-012-9452-0

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