Speaker
Description
Diagnosing epilepsy after a first unprovoked seizure, especially without visible lesions and with a normal rEEG, is challenging. Understanding EEG network changes requires data collected close to the acute event and follow-up information, necessitating a large data source. The UMG database, with over 34,000 routine EEGs, is invaluable for this purpose. In our study, it took a month to select 42 rEEGs based on specific criteria. Our results indicate that increased connectivity and power in rEEG could serve as biomarkers to predict which patients will develop epilepsy after a first seizure, potentially greatly improving quality of life and reducing treatment costs. This highlights the need for analyzing large datasets to better understand diseases. AI has the potential to significantly reduce data selection time and improve case detection, enabling faster and more efficient analysis.
