New approach to diagnosis

Beyond observation towards prediction: New approach to diagnosis and management of epilepsy

John Terry (co-founder and managing director) and Chrissie Walker (operations director) from Neuronostics explain why there is a need for new ways of diagnosing and managing epilepsy.

Over 600,000 people in the UK have epilepsy, a neurological condition characterised by recurrent seizures. Seizures are abnormal discharges of electrical activity within the brain, which typically occur infrequently and seemingly at random. Epilepsy is difficult to diagnose and hard to treat – on average it takes over a year to diagnose, misdiagnosis rates are more than 30 per cent, and over half of people still have seizures a year after beginning treatment.

Epilepsy also results in significant economic, social and personal cost. Having uncontrolled epilepsy reduces educational outcomes, employment opportunities and productivity, with salaries of people with epilepsy being on average 10 per cent lower than the median. It is estimated that epilepsy costs the NHS around £2bn per year.

Currently, the gold-standard for epilepsy diagnosis is for a clinician to observe seizures, or other abnormal brain activity, using an EEG: a clinical device that measures electrical activity of the brain. Approaching 70 per cent of the first EEG recordings taken when assessing people for suspected epilepsy show no abnormal activity. This means, for the majority of people, repeated visits to hospital for progressively longer term monitoring. These delays increase the length of time before people with epilepsy are appropriately treated, create stress and uncertainty, and result in higher costs for the healthcare system.

At Neuronostics, we are developing a full stack platform to provide decision support to healthcare professionals – improving speed, accuracy and objectivity of diagnosis and treatment of epilepsy. Our technology uses patented algorithms to interrogate EEG recordings generating the first digital biomarker of epilepsy, which we term #BioEP.

#BioEP works as follows: 1. Routine clinical data recordings (such as EEG) are uploaded directly to the

BioEP clinical platform. 2. Algorithms use these recordings to create an in silico model of the brain. 3. The model is used to understand under what conditions seizures can emerge. 4. These inform a risk score of epilepsy that provides decision support for diagnosis.

Critically #BioEP is not reliant on observing seizures. Instead our technology utilises segments of seizure-free data. Key to this are algorithms that can extract these seizure-free segments of data automatically. This minimises the burden on the healthcare professional and enhances the objectivity of the risk score provided. By revealing the risk of seizures from the very first EEG, #BioEP improves the diagnostic yield of EEG and can reduce the time taken to make an accurate diagnosis. At present there are no prognostic markers for epilepsy. Treatment outcome is essentially a case of “watchful waiting”. The #BioEP seizure risk score offers the potential for change. From EEG recordings collected prior to, and post administration of, drug treatment, variation in the #BioEP score provides a quantitative measure of response to medication. This can rapidly reveal successful treatment or highlight cases where medication is ineffective.

The #BioEP seizure risk score is based upon clearly defined equations, informed by clinical data. This means that, unlike many other AI approaches in healthcare, our risk score is transparent, providing reassurance and certainty to both healthcare professionals and people with suspected epilepsy alike.

We passionately believe in a future where epilepsy can be diagnosed quickly, and successful treatment identified, without the need to observe seizures.