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 affects more than 50 million people worldwide, making it one of theworld's most prevalent neurological diseases. The main symptom of epilepsy isseizures, which occur abruptly and can cause serious injury or death. Theability to predict the occurrence of an epileptic seizure could alleviate manyrisks and stresses people with epilepsy face. We formulate the problem ofdetecting preictal (or pre-seizure) with reference to normal EEG as a precursorto incoming seizure. To this end, we developed several supervised deep learningapproaches model to identify preictal EEG from normal EEG. We further developnovel unsupervised deep learning approaches to train the models on only normalEEG, and detecting pre-seizure EEG as an anomalous event. These deep learningmodels were trained and evaluated on two large EEG seizure datasets in aperson-specific manner. We found that both supervised and unsupervisedapproaches are feasible; however, their performance varies depending on thepatient, approach and architecture. This new line of research has the potentialto develop therapeutic interventions and save human lives.

Supervised and Unsupervised Deep Learning Approaches for EEG Seizure Prediction