Computational BioMedicine Laboratory

predictES

Category: national

Project Title: An Integrated wearable platform predicting Epileptic Seizures and novel neurofeedback ap-proaches in support of personalized patient management

Funding Organization: National Strategic Reference Framework (NSRF) 2007-2013

Programme: Cooperation 2011 - 11SYN-10-998

Coordinator: Εργαστήριο Υπολογιστικής Ιατρικής – Ινστιτούτο Πληροφορικής - Ίδρυμα Τεχνολογίας & Έρευνας (CML-FORTH)

Partners: Microelectronics – Institute of Electronic Structure and Laser - Foundation for Research and Technology (ΜRG-FORTH), Department Mother - Child - Medical School - University of Crete(MCH-UoC), Neurofeedback Institute Training (NCPD),S. Kaoukakis & others – Webcare (Webcare), Envitech Solutions Ltd (Envitech),Medotics Hellas ΕΠΕ/MEDHELLAS (Medotics)

Duration: 18/9/2013-30/10/2015

Expiration Date: 30/10/2015

Total Budget: 620000

FORTH ICS budget: 115500

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

Project Objective: 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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