What EPIC is

Epilepsy has one of the highest incidences in children under the age of five. Infantile spasms are one of the most common severe forms of epilepsy in infants, however, seizures may be difficult to recognise. Brief staring, twitching or other unusual movements may be seen as normal behaviours and get overlooked, delaying diagnosis.

However, not all healthcare professionals have experience in suspecting infantile spasms. Although early diagnosis and prompt treatment may prevent adverse neurodevelopmental outcomes, families and charities representing infantile spasms have reported delays in diagnosis.

To confirm or rule out infantile spasms, an EEG (electroencephalogram) is required. However, the timely availability of EEG appointments, clinicians who can review EEG results, or hospital beds is varied. Additionally, access inequalities greatly affect families living in remote areas and/or deprived backgrounds, further delaying diagnosis. 

In recent years, ambulatory EEGs have been increasingly conducted by the hospital team in the patient’s home, however, this approach is highly labour-intensive and time-consuming.

Our team of researchers (including health professionals, engineers and scientists) are working alongside clinicians and families to create a ‘remote’ EEG, with in-built Artificial Intelligence (AI) detection software, that will be available at home for the family to use, or at the GP.

This could allow families to have an EEG screening more quickly if they suspect their child may have epilepsy, and to monitor the effect of treatment without having to travel repeatedly to the hospital.

• To work with families and healthcare professionals to identify what is preventing the early diagnosis and treatment of childhood epilepsies in the community, and to co-deliver solutions.
• To co-develop ways to automatically and efficiently detect changes in brain activity through an EEG, caused by early stages of childhood epilepsies.
• To co-create AI-based solutions for more accurate and patient-specific epilepsy management.
• To demonstrate that our solution reduces health inequalities by enabling the detection, monitoring and prediction of response to treatment in childhood epilepsies in the community.