Εργαστήριο Υπολογιστικής Βιο-Ιατρικής

predictES

Κατηγορία: national

Τίτλος Έργου: Ολοκληρωμένη φορητή πλατφόρμα για την πρόβλεψη επιληπτικών κρίσεων και καινοτόμες προσεγγίσεις νευροανάδρασης για την υποστήριξη εξατομικευμένης φροντίδας ασθενών

Φορέας Χρηματοδότησης: ΕΣΠΑ 2007-2013

Είδος έργου: ΣΥΝΕΡΓΑΣΙΑ 2011 - 11SYN-10-998

Συντονιστής/Ανάδοχος: Computational Medicine Laboratory - Institute of Computer Science - Foundation for Research and Technology (CML-FORTH)

Διάρκεια: 18/9/2013-30/10/2015

Ημ/νία Λήξης: 30/10/2015

Συνολικός προϋπολογισμός έργου: 620000

Προϋπολογισμός ΙΤΕ-ΙΠ: 115500

Web Site: https://atlas.ics.forth.gr/predictes

Στόχος έργου: Epilepsy is one of the most common neurological disorders, with a prevalence of 0.6–0.8% of the world’s population1. Anticonvulsive medication and epilepsy surgery offers seizure freedom to the majority of epileptic patients but still for a remaining 25% of patients, no sufficient treatment is currently available. The most disabling aspect for every epileptic patient is the central characteristic of this disease, the abrupt and almost unpredictable onset of seizures. As a result, the increased risk of serious injury, helplessness, embarrassment and social disability regarding driving, professional orientation and relationships render everyday activities complicated and significantly reduce the patient’s quality of life. To improve quality of life, especially for the non-responsive to treatment patients, it is crucial to develop innovative, non-invasive seizure prediction technologies so as to enable timely alarming of patients and prevention of life-threatening consequences. To address this important health and societal problem, we propose the development of an innovative integrated wearable platform and the associated SW components for seizure prediction. In successfully addressing this challenge, the project focuses on developing novel sensing technologies concerning improved recording of brain activity; multi-sensorial information fusion and novel prediction algorithms integrated as a wearable platform for everyday life use. In the context of improving the quality of life of epileptic patients the project will also focus on the application of Neurofeedback, an approach that has the potential to restore brain wave activity back to normal patterns. In achieving this second objective the project focuses on the implementation of novel information processing algorithms for improving optimal coherence and phase synchronization identification, so as to reduce the likelihood of seizure occurrence.

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