PROJECT GRANT
£148,372 over 24 months
Awarded in
2018

SCIENTIFIC TITLE

Ultra-longterm serial EEG: association of a novel seizure likelihood index with seizure occurrence, stress, sleep and drug dose

LEAD INVESTIGATOR

Professor Mark Richardson

CO-INVESTIGATORS

Dr David Lester, Dr Frank Zanow

INSTITUTION

King’s College London

Living with epilepsy involves living with the uncertainty about when the next seizure will happen. Research work has been going on for nearly 30 years, to find ways to “forecast” when an individual person will have their next seizure. Excitingly, some fantastic progress has been made in the last few years. We hope to build on this progress, to find a way to forecast seizures using the latest generation of home EEG and smartwatches. We hope to see seizure forecasting become a reality within the next few years.
Professor Mark Richardson

Background

Some people with epilepsy notice they are more likely to have seizures if they are tired, stressed, or have missed medication. If we could measure the influence of these factors on the brain, we might be able to use this information to forecast when seizures are more likely to happen. Recently, it has been shown that signals from EEG can forecast when the next seizure will happen. However, this approach requires EEG to be recorded all the time, which is not likely to be acceptable.

The Study

It is now possible to record your own EEG easily, using a cap attached to a miniature recording system. Prof Richardson believes that people with epilepsy could easily learn how to record their own EEG in a few minutes. In this project, researchers will study a group of people with epilepsy over several months. They will collect information about sleep, stress and medication. Participants will be asked to record their own EEG at home for 10 minutes, twice every day, and will wear a smartwatch device that records movement and heart rate. They will also note when they take medication and when seizures occur. The researchers aim to combine all of this information to see if it could be used to reliably forecast when seizures are likely to happen. They will test whether signals in the twice-daily EEG, and signals from the smartwatch regarding sleep and stress, forecast when seizures will happen.

Relevance

One of the most difficult aspects of epilepsy is that seizures seem to strike “out of the blue”, with no warning. If we could forecast when seizures are more likely to happen, it could make a major difference for many people with epilepsy. The researchers hope that outcomes from this study will enable seizure forecasting within the next few years.

Prof Richardson’s project has been supported by our memorial funds. We would like to thank all our memorial fund supporters for their tremendous efforts and generosity.