Title: BioEP: From prototype to clinical evaluation
Funder: Department of Health and Social Care

Abstract: Background 125,000 people attend a first seizure clinic annually with suspected epilepsy. 40,000 eventually receive an epilepsy diagnosis following an average delay of 1 year and with a misdiagnosis rate of 30%. The Electroencephalogram is a primary clinical tool for revealing brain abnormalities indicative of epilepsy, however 70% of diagnostic EEGs appear clinically normal. To address this, we have developed a novel computer BIOmarker of EPilepsy: BioEP, that can reveal seizure risk from apparently normal EEG. Aims and Objectives The main aim of this phase II project is to develop our existing BioEP prototype to a minimum viable product (MVP) and to secure CE marking. The key objectives are to: &bull; use EEG and clinical meta-data (including diagnosis) to develop machine learning methods to further increase accuracy of the BioEP seizure risk score; &bull; develop the BioEP platform in collaboration with clinical users and people with lived experience; &bull; evaluate the clinical utility of the technology through user testing; &bull; gather necessary evidence for CE marking. Project plan and methods used Our project is split into six worpackages spanning algorithm development, platform development, clinical evaluation, regulation and compliance, commercialisation and reporting and dissemination. The algorithms we will develop utilise a number of machine learning approaches including random forest, automatic relevance determination (ARD), Bayesian shrinkage methods, least absolute shrinkage and selection operator (LASSO) regression and K-means clustering. The BioEP platform will be developed utilising Microsoft's .Net framework. Timelines for delivery We will complete the project in 24 months. Anticipated Impact and Dissemination We will deliver an MVP with a detailed product specification document and clinical evaluation file. Research findings and clinical evaluation studies will be communicated in peer-reviewed publications and at scientific meetings. We will work closely with key stakeholders, including those with lived experience, to disseminate findings through relevant networks.

Lay abstract: Background to the development work Our proposal is focussed on Epilepsy: a serious neurological condition. Epilepsy is characterised by the tendency to have repeated, spontaneous, seizures. Since seizures occur apparently at random, epilepsy diagnosis is very challenging. The electroencephalogram (EEG) is an important clinical tool for revealing abnormalities that indicate epilepsy. However, around 70% of recorded EEGs provide no clinical indication of epilepsy. Consequently, diagnostic delays regularly exceed a year and the rate of misdiagnosis is estimated at 30%. The cost of this misdiagnosis has been estimated at over &pound;300M annually to the NHS. To address this challenge, we have developed a computer Biomarker of EPilepsy: BioEP. BioEP works by integrating routinely acquired EEG into a computer model of the brain. Simulations of this model, interrogated using advanced machine learning algorithms, provide a risk score showing how easy it is for seizures to occur. For EEGs currently considered clinically negative, the BioEP risk score provides valuable additional diagnostic information. Research underpinning BioEP has been recognised by the Lancet Neurology (as a breakthrough study in epilepsy in 2015) and by the Epilepsy Foundation (through a commercialisation prize in 2017). Through Innovate UK funding, we have developed a prototype of a diagnostic decision support tool powered by BioEP, that enables semi-automated feature extraction and report writing. As part of this project, the clinical utility of BioEP has been established in a study of over 100 subjects. Aim(s) of the development work The aims of this Phase II project are: &bull; to develop our current prototype to a minimum viable product through engagement with key stakeholders; &bull; to use use EEG and clinical meta-data (including diagnosis) from neurology centres to develop machine learning methods to further increase accuracy of the BioEP seizure risk score; &bull; to evaluate the clinical utility of the product; &bull; to gather necessary evidence for CE marking. Patient and public involvement We will expand our involvement and engagement activities with key stakeholders, including clinicians and people with lived experience, through the creation of user and lived experience groups. Their input will be critical to enable refinement and improvement of our technology. Dissemination and anticipated outcomes Our primary output is a tool that supports diagnosis and prognosis of epilepsy by revealing features within EEG currently hidden to standard clinical practice. We will secure necessary regulatory approvals confirming the BioEP platform is high quality and safe to use. This will accelerate its uptake in neurology centres throughout the NHS. To further advance clinical acceptance, research findings will be published in peer-reviewed journals and presented at international conferences. Our engagement activities with key stakeholders will also help inform our understanding of the needs of these groups when developing marketing and educational resources.

Research activity codes: 4.2 EVALUATION OF MARKERS AND TECHNOLOGIES;7.3 MANAGEMENT AND DECISION MAKING;4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES
Health categories: NEUROLOGICAL