logo

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

Sector: Hospital • Location: Ireland

Source: EU Funding & Tenders Portal

Project
Ended

Epilepsy is one of the most common nervous system disorders and affects more than 50M people worldwide. The majority of new-onset cases occur in elderly and children. 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 ther

Project Information FAQ

Project Information

3 Q
The project “Epileptic Seizure Prediction System (ESPS) - Predicting and preventing epileptic seizures based on respiratory biofeedback & machine learning.” is an infrastructure initiative in the Hospital sector, located in Ireland. Taiyo aggregates data on it from EU Funding & Tenders Portal.

Want to explore the full details? View the full report

Participants

Sponsoring Agency

Obfuscated Data

Company

Obfuscated Data

Status

Original status

ended

Taiyo status

Obfuscated Data

Taiyo last update

00-00-0000

Available timestamps

00-00-0000

Available timestamp type

Obfuscated Data

Contact

Contact name

Obfuscated Data

Phone

0000000000

Email

ObfuscatedData@email.com

Address

Obfuscated Data, Obfuscated data, obfuscated data, Obfuscated data

Description

Description

Epilepsy is one of the most common nervous system disorders and affects more than 50M people worldwide. The majority of new-onset cases occur in elderly and children. 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. 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. Reliably predicting seizures enables an independent life for the patient and at the same time reduces healthcare costs caused by clinician visits, injuries & caretaking. iBreve’s new patent-pending wearable technology analyzes respiratory patterns in real-time, enabling market applications for seizure prediction, respiratory treatment & stress management. iBreve’s technology received interest from Harvard’s Boston Children’s Hospital to be included in clinical trials for seizure detection & prediction. The ESPS machine learning algorithm calculates seizure probability & intensity and if desired an alert is sent to the patient’s caregiver. The tracking and analysis of seizures can be shared with clinicians & allows to personalize treatment. Main objective of this feasibility study is the development of a comprehensive business plan to evaluate the opportunities & risks of introducing ESPS into the homecare market. All project activities follow the Healthcare Innovation Cycle methodology & are structured around 4 development pillars - Technology, Market & Business, Clinical and Regulatory. The introduction of ESPS is prone to disrupt the epilepsy market by reducing treatment costs per patient by up to 70%. Thus, saving 14B€ in health care costs in Europe alone and creating a major shift towards preventive & personalized treatment.

Original sub-sector

Obfuscated

Original Currency

USD

Original budget

000000000000000

Procurement method

Obfuscated Data

Budget

000000000000000

Location

Region

Obfuscated

Country

Obfuscated

State

Obfuscated Data

County

Obfuscated

Location

Obfuscated Data, Obfuscated data, obfuscated data, Obfuscated data

Source

Source reliability

High

Data quality score

100%

Source

Obfuscated Data

URL

obfuscated_data,obfuscateddata.com

More Details

Project Type

Obfuscated Data

Article Published Date

Obfuscated Data