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. 2019 Aug;13(4):325-339.
doi: 10.1007/s11571-019-09527-y. Epub 2019 Mar 15.

Complex network based models of ECoG signals for detection of induced epileptic seizures in rats

Zeynab Mohammadpoory  1 , Mahda Nasrolahzadeh  1 , Naghmeh Mahmoodian  1   2 , Mohammad Sayyah  3 , Javad Haddadnia  1
Affiliations

Complex network based models of ECoG signals for detection of induced epileptic seizures in rats

Zeynab Mohammadpoory et al. Cogn Neurodyn. 2019 Aug.

Abstract

The automatic detection of seizures bears a considerable significance in epileptic diagnosis as it can efficiently lead to a considerable reduction of the workload of the medical staff. The present study aims at automatic detecting epileptic seizures in epileptic rats. To this end, seizures were induced in rats implementing the pentylenetetrazole model, with the electrocorticogram (ECoG) signals during, before and after the seizure periods being recorded. For this purpose, five algorithms for transforming time series into complex networks based on visibility graph (VG) algorithm were used. In this study, VG based methods were used for the first time to analyze ECoG signals in rats. Afterward, Standard measures in network science (graph properties) were made to examine the topological structure of these networks produced on the basis of ECoG signals. Then these measures were given to a classifier as input features so that the ECoG signals could be classified into seizure periods and seizure-free periods. Artificial Neural Network, considered a popular classifier, was used in this work. The experimental results showed that the method managed to detect epileptic seizure in rats with a high accuracy of 92.13%. Our proposed method was also applied to the recorded EEG signals from Bonn database to show the efficiency of the proposed method for human seizure detection.

Keywords: Complex network; Epileptic rat; Induced epilepsy; Pntylenetetrazole (PTZ); Seizure detection.

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Figures

Fig. 1
Fig. 1
An example of the NVG construction. The upper part shows time series and lower part represents the NVG (Lacasa et al. 2008)
Fig. 2
Fig. 2
An example of the HVG construction. The upper part shows time series and lower part represents the HVG (Luque et al. 2009)
Fig. 3
Fig. 3
Illustrates the procedure of converting time series to the LPVG (N = 1). a Time series: each black line shows the two connected points can see each other. The red lines are extra connections in LPVG compared with VG. b Corresponding graph: The LPVG consists of red and black links and the NVG consists only of black links (Pei et al. 2014). (Color figure online)
Fig. 4
Fig. 4
The Illustration of the PNVG algorithm. a Time series plot for NVG and PNVG algorithms, b corresponding graphs, The NVG consists of red and blue links. The PNVG (π/2) consists only of red links. Upper left—the PNVG link selection criterion for view angle α = π/2 applied to nodes (ti) (Bezsudnov and Snarskii 2014). (Color figure online)
Fig. 5
Fig. 5
An example of the Markov-binary visibility graph construction. The upper part shows time series and intermediate part shows the Markov binary sequence generated from time series. The bottom part represents the graph generated through the visibility algorithm (Ahadpour et al. 2014)
Fig. 6
Fig. 6
The long-term ECoG recordings of a rat in the test group. Seizure interval is indicated in this Figure
See this image and copyright information in PMC

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References

    1. Abarbanel HDI, Brown R, Sidorowich JJ, Tsimring LS. The analysis of observed chaotic data in physical systems. Rev Mod Phys. 1993;65:1331.
    1. Acharya UR, Sree SV, Suri JS. Automatic detection of epileptic EEG signals using higher ordercumulant features. Int J Neural Syst. 2011;21(5):403–414. - PubMed
    1. Acharya UR, Sree SV, Chattopadhyay S, Yu W, Ang PC. Application of recurrence quantification analysis for the automated identification of epileptic EEG signals. Int J Neural Syst. 2011;21(3):199–211. - PubMed
    1. Adeli H, Zhou Z, Dadmehr N. Analysis of EEG records in an epileptic patient using wavelet transform. J Neurosci Methods. 2003;123(1):69–87. - PubMed
    1. Adeli H, Ghosh-Dastidar S, Dadmehr N. A wavelet-chaos methodology for analysis of EEGs and EEG subbands to detect seizure and epilepsy. IEEE Trans Biomed Eng. 2007;54(2):205–211. - PubMed

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