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Dynamical analysis of epileptic characteristics based on recurrence quantification of SEEG recordings

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  • Yang, Chuanzuo
  • Luan, Guoming
  • Liu, Zhao
  • Wang, Qingyun

Abstract

The evolution of epilepsy is always accompanied with the transitions of dynamics. Characterizing these dynamical processes can be beneficial for understanding the mechanism of seizures. Meanwhile, there also exist dynamical differences between regions, especially in the epileptogenic and non-epileptogenic areas. Hence, in this study stereo-electroencephalograph (SEEG) recordings from 10 patients with refractory focal epilepsy were collected, and recurrence plot was used to investigate the dynamical differences between different epilepsy stages as well as regions. All dynamical characteristics were quantified by means of recurrence quantification analysis. Furthermore, synchronization between channels were also revealed through cross recurrence plot. Results suggested that almost all channels in the pre-ictal and ictal stages had higher recurrence rate than those in the inter-ictal. And epileptogenic channels were identified with longer diagonal structures, which indicated that recordings from epileptogenic regions were more deterministic and recurrent. When seizures occurred, the synchronizations between these epileptogenic channels were strengthened and dominated the dynamics of epileptic brain. This might provide additional insights into the dynamical nature of epileptic phenomena.

Suggested Citation

  • Yang, Chuanzuo & Luan, Guoming & Liu, Zhao & Wang, Qingyun, 2019. "Dynamical analysis of epileptic characteristics based on recurrence quantification of SEEG recordings," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 507-515.
    • Handle: RePEc:eee:phsmap:v:523:y:2019:i:c:p:507-515
    DOI: 10.1016/j.physa.2019.02.017
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    References listed on IDEAS

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    1. Yan, Jiaqing & Wang, Yinghua & Ouyang, Gaoxiang & Yu, Tao & Li, Xiaoli, 2016. "Using max entropy ratio of recurrence plot to measure electrocorticogram changes in epilepsy patients," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 443(C), pages 109-116.
    2. Narin, Ali & Isler, Yalcin & Ozer, Mahmut & Perc, Matjaž, 2018. "Early prediction of paroxysmal atrial fibrillation based on short-term heart rate variability," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 56-65.
    3. Yu, Haitao & Cai, Lihui & Wu, Xinyu & Song, Zhenxi & Wang, Jiang & Xia, Zijie & Liu, Jing & Cao, Yibin, 2018. "Investigation of phase synchronization of interictal EEG in right temporal lobe epilepsy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 931-940.
    4. Ginoux, Jean-Marc & Ruskeepää, Heikki & Perc, Matjaž & Naeck, Roomila & Costanzo, Véronique Di & Bouchouicha, Moez & Fnaiech, Farhat & Sayadi, Mounir & Hamdi, Takoua, 2018. "Is type 1 diabetes a chaotic phenomenon?," Chaos, Solitons & Fractals, Elsevier, vol. 111(C), pages 198-205.
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    Cited by:

    1. Sun, Biao & Lv, Jia-Jun & Rui, Lin-Ge & Yang, Yu-Xuan & Chen, Yun-Gang & Ma, Chao & Gao, Zhong-Ke, 2021. "Seizure prediction in scalp EEG based channel attention dual-input convolutional neural network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 584(C).

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