English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Epilepsy is a neurological disorder arising from anomalies of the electricalactivity in the brain, affecting about 0.5--0.8\% of the world population.Several studies investigated the relationship between seizures and brainwavesynchronization patterns, pursuing the possibility of identifying interictal,preictal, ictal and postictal states. In this work, we introduce a graph-basedmodel of the brain interactions developed to study synchronization patterns inthe electroencephalogram (EEG) signals. The aim is to develop apatient-specific approach, also for a real-time use, for the prediction ofepileptic seizures' occurrences. Different synchronization measures of the EEGsignals and easily computable functions able to capture in real-time thevariations of EEG synchronization have been considered. Both standard andad-hoc classification algorithms have been developed and used. Results on scalpEEG signals show that this simple and computationally viable processing is ableto highlight the changes in the synchronization corresponding to the preictalstate.

Anticipating epileptic seizures through the analysis of EEG synchronization as a data classification problem