Epileptic Seizure Prediction System (ESPS) - Predicting and preventing epileptic seizures based on respiratory biofeedback & machine learning.

Project Information

ESPS

Grant agreement ID: 876106

DOI

10.3030/876106

Project closed

EC signature date 21 July 2019

Start date 1 July 2019

End date 31 December 2019

Funded under

• INDUSTRIAL LEADERSHIP - Innovation In SMEs

Total cost

€ 71 429,00

EU contribution

€ 50 000,00

Coordinated by

IBREVE LIMITED
Ireland

Periodic Reporting for period 1 - ESPS (Epileptic Seizure Prediction System (ESPS) - Predicting and preventing epileptic seizures based on respiratory biofeedback & machine learning.)

Reporting period: 2019-07-01 to 2019-12-31

Summary of the context and overall objectives of the project

Epilepsy is one of the most common nervous system disorders and affects more than 50M people worldwide. Currently there is no cure for epilepsy. Although antiepileptic drugs can help, one third of all patients do not respond to any pharmacological intervention. This staggering number has not changed in decades, despite over 14 new therapies entering the market.
Seizures that lasts for more than a brief period of time are a medical emergency and can lead to death. Each year, more than 1 out of 1,000 people with epilepsy die from sudden unexpected death in epilepsy (SUDEP). If seizures are uncontrolled, the risk of SUDEP increases to more than 1 out of 150.
Importance for Society: Approximately 8-10% of people will experience an epileptic seizure during their lifetime. The majority of new-onset cases occur in elderly and children. The unpredictable nature of seizures causes the largest burden for patients as it literally disrupts their lives. Patients with several seizures a week are hesitant to leave the house & find it hard to obtain employment.
Main objective of this feasibility study was the development of a comprehensive business plan to evaluate the opportunities & risks of introducing ESPS into the homecare market. The results of the feasibility study manifest the project’s technological, practical as well as economical feasibility.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

The performed work & results follow the Healthcare Innovation Cycle methodology & were structured around the 4 development pillars - Business, Technology, Clinical and Regulatory. A comprehensive business plan has been developed, based on conducted interviews, performed re-risking experiments & advice from experts in the field. The results of the feasibility study manifest the project’s technological, practical as well as economical feasibility of its breakthrough concept.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

The unpredictable nature of seizures has the largest impact on patient’s quality of life. Patients with several seizures a week are hesitant to leave the house & find it hard to obtain employment. Reliably predicting seizures enables a more independent life, allows participation in social life outside the homes and reduces healthcare costs caused by injuries. ESPS’s new patent-pending wearable technology analyzes respiratory patterns in real-time, enabling market applications for seizure monitoring, respiratory treatment & stress management.

The wearable helping with stress, anxiety & chronic disease management