Abstract
This paper addressed the issue of epileptic absence seizures developing a complex brain network model based on the estimation of the coherence between electroencephalographic (EEG) signals. The EEG signals indeed reflect the abnormalities in the cortical electrical activity caused by epilepsy. A dataset of 10 absence patients was analyzed, including 63 seizures. The model was analyzed over the time to assess if changes in the network parameters matched the brain state (ictal: seizure), (non-ictal: seizure free). During the ictal states, the characteristic path length (\(\lambda \)) decreased and the global efficiency (GE), the average clustering coefficient (CC) and the small worldness (SW) increased, as expected, because of the abnormal synchronization associated with absence seizure onset. The connection matrices preceding the ictal states (8 s before) were thresholded and the corresponding connectivity scalp maps, showing the active links between EEG channels, were displayed. Such connectivity maps showed the interaction between channels and provided information about the abnormal recruitment mechanism associated with seizure development: the involvement of the cortical areas appears progressive and that every subject exhibited peculiar recurrent patterns of area activation.
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Nadia Mammone’s work was funded by the Italian Ministry of Health, Project Code GR-2011-02351397.
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Mammone, N., Ieracitano, C., Duun-Henriksen, J., Kjaer, T.W., Morabito, F.C. (2019). Coherence-Based Complex Network Analysis of Absence Seizure EEG Signals. In: Esposito, A., Faundez-Zanuy, M., Morabito, F., Pasero, E. (eds) Quantifying and Processing Biomedical and Behavioral Signals. WIRN 2017 2017. Smart Innovation, Systems and Technologies, vol 103. Springer, Cham. https://doi.org/10.1007/978-3-319-95095-2_14
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