Enabling the early and equitable diagnosis of epilepsy in infants in the community (EPIC)

EPIC is a 3-year multidisciplinary research project designed to meet the urgent need for early and equitable diagnosis of epilepsy in infants.

Summary

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. 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 are increasingly conducted by the hospital team in the patient’s home, however, this approach is highly labour-intensive and time-consuming. For these reasons, our team of researchers (including health professionals, engineers and scientists) will work alongside clinicians and families to create a ‘remote’ EEG, with in-built Artificial Intelligence (AI) detection software, that will be available in community settings (i.e., 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.

EPIC project logo with strapline text

Project Aims

  1. To partner with families and clinicians to identify barriers preventing the early detection and management of childhood epilepsies in the community, and to co-deliver solutions to them.
  2. To co-develop methods to automatically and efficiently measure changes in EEG brain activity due to early stages of childhood epilepsies.
  3. To co-create AI methods for more accurate and patient-specific management.
  4. 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.

EPIC is a multidisciplinary collaboration, led by the School of Engineering, with co-leads at NHS Lothian and the Usher Institute. Scroll down to the "Key People" heading to learn more about the team.

EPIC Family and EPIC Clinician

Before co-developing the remote EEG device, the EPIC team want to understand families and clinicians’ perspectives on current diagnostic pathways (what works well and what could be improved) as well as their views on the potential remote EEG device. To do this, they are organising opportunities for families, carers and clinicians to engage with their research.

Details of upcoming activities are listed on the project website. If you are interested in taking part, please contact the team directly.

Visit the EPIC project website for full details

A group of ten people smiling and waving
EPIC project team, January 2026

Key People

NameRolesUniversity of Edinburgh department
Javier EscuderoProject Lead | Reader in Biomedical Signal ProcessingSchool of Engineering
Jay ShettyClinical Co-Lead | Consultant Paediatric NeurologistInstitute for Regeneration and Repair
Laura SmithPatient and Public Involvement and Engagement Co-Lead | Research Group CoordinatorUsher Institute
Samantha MarinelloPatient Representative 
Alfredo Gonzalez-SulserProject Co-Lead | Senior LecturerInstitute for Neuroscience and Cardiovascular Research
Tsz-Yan Milly LoProject Co-Lead | Honorary Reader | Consultant Paediatric IntensivistUsher Institute
Andrew StanfieldProject Co-Lead | Senior Clinical Research FellowInstitute for Neuroscience and Cardiovascular Research
Sotirios TsaftarisProject Co-Lead | Professor of Machine Learning and Computer VisionInstitute for Imaging, Data and Communications

Postdoctoral Research Associate

Institute for Imaging, Data and Communications

Research Assistant

Institute for Regeneration and Repair 

Max RowntreeElectrophysiologistInstitute for Regeneration and Repair
Edward MoroshkoPostdoctoral Research AssociateInstitute for Imaging, Data and Communications

Contact details

Website

Key Publications

Publications from this project can be found on the Principal Investigator's Edinburgh Research Explorer page.

Key Collaborations

Partners and Funders

This work was supported by the Engineering and Physical Sciences Research Council [Grant number UKRI1659].

Project Timeline

January 2026 – December 2028

Themes and Keywords

Scientific Themes

Artificial Intelligence; Epilepsy; Health Inequality; Paediatrics; Signal Processing

Methodology Keywords

Artificial Intelligence; Co-production