Funding boost for AI-based epilepsy monitoring

graphical illustration of networks in the brain

Neuronostics, a spinout formed to develop faster, more accurate diagnostic tools for epilepsy, has received funding to develop its BioEP platform – an AI-based system for enhanced diagnosis and treatment response monitoring.

Led by Professor John Terry, co-founder of the company and Director of the Centre for Systems Modelling & Quantitative Biomedicine at BHP founder-member the University of Birmingham, BioEP works by creating mathematical models of the brain using short segments of electroencephalogram (EEG) recordings. Computer simulations rapidly reveal the ease with which seizures can emerge and form the basis of the BioEP seizure risk score.

Professor Terry’s research aims to improve diagnosis and treatment for people with epilepsy.  He explains:  “We build personalised models of the brain using EEG that is routinely collected when seeking to diagnose epilepsy. From these models the risk of epilepsy can be quickly determined.

“In contrast, multiple EEG recordings are often required to reach a clinical diagnosis at present. This is expensive, time-consuming, and exposes people with suspected epilepsy to risk.”

EEG from a patient with epilepsy
Example of an EEG from a patient with epilepsy

The funding, from the National Institute for Health Research (NIHR), will enable the research partnership to progress a prototype clinical platform that can provide a risk score showing the individual’s susceptibility to seizures.  This measurement can be used in diagnosis, and as an objective assessment of response to treatment with anti-epileptic drugs, resulting in faster seizure control for people with epilepsy.

The clinical utility of the BioEP seizure risk score has already been demonstrated in a cohort of people with idiopathic generalised epilepsy.1  Using just 20 seconds of an EEG recording that would be considered inconclusive in the current clinical pathway, BioEP achieved 72% diagnostic accuracy.  This matches the accuracy achieved in the current diagnostic pathway, which typically takes a year, and involves multiple follow-ups.2

The company is interested to hear from commercial partners in EEG hardware manufacturing, digital EEG analysis, and companion diagnostics or prognostics, and research and clinical partners with interests in epilepsy, traumatic brain injury and dementia.

The NIHR funding was delivered through the AI in Health and Care Award, part of the NHS AI Lab, which was launched by the UK Government earlier this year to accelerate the adoption of Artificial Intelligence in health and care.

References

      1. H Schmidt et al. A computational biomarker of idiopathic generalized epilepsy from resting state EEG Epilepsia 57: e200-e204 (2016).
      2. S Smith. EEG in the diagnosis, classification, and management of patients with epilepsy Journal of Neurology, Neurosurgery & Psychiatry 76: ii2-ii7 (2005).