The mobile diagnostic system is based on the application of state-of-the-art learning methods with artificial intelligence
Goal
The goal is the technical realization and clinical testing of a body-worn sensor network for the automated detection and prediction of epileptic seizures using state-of-the-art learning methods from the field of artificial intelligence. A mobile diagnosis and prognosis system is intended to give affected patients back more independence and quality of life and reduce the risk of accidents and injuries (prevention). At the same time, long-term monitoring should improve the quality of treatment for epilepsy patients: Frequency and type of seizures can be extracted from the data and thus the origin of the electrical discharges in the brain can be inferred (diagnosis and therapy, main focus). These findings in turn have an impact on a possible specific adaptation of the sensors and discharge of the patients into the home environment (aftercare).
Duration
05/2022 – 10/2024
Funding
upperVision2030 – Digital Health – The Digital Patient Journey (Land OÖ / FFG),
FFG Projectnumber
Partners
Kepler Universitätsklinikum GmbH, Klinik für Neurologie 1- Neuromed Campus (KUK)
Johannes Kepler Universität Linz, Institut für Machine Learning (IML)
FiveSquare GmbH (FSQ)
FO999892171
Contact Wolfgang Narzt