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Detection of epileptic seizures with anticipation

Grant number: 16/08272-8
Support Opportunities:Research Grants - Innovative Research in Small Business - PIPE
Duration: November 01, 2017 - July 31, 2018
Field of knowledge:Engineering - Electrical Engineering - Electrical, Magnetic and Electronic Measurements, Instrumentation
Principal Investigator:Hilda Alicia Gomez
Grantee:Hilda Alicia Gomez
Host Company:Gomez & Gomez Ltda. - ME
City: São Paulo
Associated researchers:Giuliano Leite de Vitor
Associated grant(s):18/19877-3 - Medical device for epileptic seizures prediction, AP.PIPE
Associated scholarship(s):18/00300-8 - Epiletic seizures early detection system, BP.TT


One of the biggest challenges for those studying epilepsy is predicting when a seizure will happen. Epistemic is developing a device that does exactly that: informs the patient of an incoming seizure 25 minutes in advance.The method used by Epistemic is non-invasive and the corresponding algorithm has been tested with information collected from electroencephalograms from patients with epilepsy. The results had a 90% assertiveness. The first warnings occurred with an average of 25 minutes in advance. The idea is to use this algorithm to develop a device for daily use: a portable device that can be used in the day-to-day and that warns a person of an imminent seizure.This method, used in a portable device, can improve the quality of life of people with epilepsy. Opening doors for a more autonomous life and being able to count with help BEFORE the seizure happens. In addition to warning the user, the device is being planned to send a message to an app that can be installed in anyone's smartphone. For children with epilepsy, this person could be one of the parents.The advanced warning allows people with epilepsy to perform activities that they currently can't, such as riding a bike or driving. If the person is warned of the seizure in advance, they can normally perform such activities and stop when they are warned of a seizure. If appropriate, the person could even take a prescribed medication.The device consists of two electrodes connected to a processor and a wearable device. The electrodes detect and send EEG signals to the processor. The latter has a software that is constantly running and analyzing the signals. If there is an anomaly, it sends a warning to the patient's wearable, which in turn resends this warning to other registered mobile devices.The detection methodology has been tested and corroborated. The main focus of this project is analyzing the viability of the hardware. (AU)

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