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.

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, with co-leads based at the School of Engineering, 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 asking families and clinicians to complete an online survey, join a focus group or attend an engagement event.

EPIC Family

Families with experience of EEG testing for their child aged 2 years or less, are invited to complete an online survey.

An information sheet for EPIC Family and link to the survey will be added to this page in due course.

EPIC Clinician

Health professionals who are involved in clinical assessment and requesting, performing or reviewing EEGs in children less than 2-years of age, are invited to attend an in-person or online engagement event. 

Information on the Clinician Engagement Events will be shared in due course.

Key People

NameRolesUniversity of Edinburgh department
Javier Escudero RodriguezProject Lead, Read in Biomedical Signal ProcessingSchool of Engineering
Jay ShettyProject Co-Lead, Consultant Paediatric NeurologistInstitute for Regeneration and Repair
Laura SmithProject Co-Lead, Research Group CoordinatorUsher Institute
Iva PehResearch AssistantInstitute for Regeneration and Repair
Samantha MarinelloPatient Representative 
Alfredo Gonzalez-SulserProject Co-Lead, Senior LecturerInstitute for Regeneration and Repair
Tsz-Yan Milly LoProject Co-Lead, Honorary Reader and Consultant Paediatric IntensivistUsher Institute
Andrew StanfieldProject Co-Lead, Senior Clinical Research FellowInstitute for Regeneration and Repair
Sotirios TsaftarisProject Co-Lead, Professor of Machine Learning and Computer VisionInstitute for Imaging, Data and Communications

Contact details

Key Publications

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

Key Collaborations

  • Epilepsy Research Institute UK
  • Epilepsy Scotland
  • UK Infantile Spasms Trust
  • SYNGAP1 UK
  • BrainsView

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

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

Methodology Keywords

Co-production; Artificial Intelligence