This SBIR Phase I project entails the development of an EEG analysis software application that identifies the epileptogenic zone (EZ - where seizures start in brain) in medically refractory epilepsy (MRE) patients. Over 1 million people in the US have MRE, meaning that they do not respond to medication. MRE patients are frequently hospitalized, burdened by epilepsy-related disabilities, and contribute to 80% of the $16 billion dollars spent annually in the US treating epilepsy patients. There are 2 treatments: (i) surgical removal of the EZ, and (ii) neurostimulation, where the EZ is electrically stimulated to suppress seizures. Successful outcomes depend critically on accurately identifying the EZ from invasive EEG recordings, which is a long costly process, leading to grim outcomes where 30%-70% of treated patients continue to have seizures. There has thus been an intensive search for an accurate data analytics tool to reduce time, risks and costs of invasive monitoring. This project involves further development of such a tool that generates visual "heat" maps from EEG data. The tool, grounded in dynamical systems theory and neuroengineering, has been validated with data from 20 patients, achieving 95% accuracy in predicting surgical outcomes. Reducing monitoring time reduces the risk of infection from the brain being exposed, and reduces hospital costs associated with lengthy stays and clinical staff reviewing data. By providing more accurate definition of the EZ, the tool will also enable use of a precise and entirely new laser ablation procedure that makes tiny lesions in targeted structures as opposed to removing large portions of the brain. If successful, the tool will be closer to commercialization under a sustainable business model. Major EEG vendors and medical device companies are looking for accurate software applications in epilepsy treatment to enhance their product suites, and will be very interested in licensing the tool. This Small Business Innovation Research Phase I project involves development of a cutting-edge EEG tool that uses dynamic network modeling and a highly innovative and patented theory of "fragility" of nodes in a dynamic network to localize the EZ from invasive EEG recordings, taking into account the extensive interconnection of neurons in the brain. The more "fragile" an EEG channel, the more likely it is in the EZ. Project aims are to (i) validate the tool on a large patient cohort, using invasive EEG data before, during and after seizure events; (i) test the tool?s efficacy using noninvasive scalp EEG recordings and (iii) design the user-interface and integrate this application into the existing clinical workflow to facilitate prospective studies. These milestones will minimize key risks in bringing this innovation to market, which are adoption, perceived liability, regulatory approval and reimbursement. Adoption risk will be mitigated if the tool is accurate, quick and easy-to-use, requiring essentially the push of a button to receive fragility maps. Accuracy risk will be mitigated if our completed retrospective study, including refinement of network models, shows comparable performance to our preliminary data. The quick and easy-to-use risks will be mitigated with the development of an intuitive interface that importantly integrates with the existing EEG data acquisition and visualization tools. Regulatory risk is low as a predicate device exists. Perceived liability of the tool in mis-diagnosis is a low risk as the tool is not intended to replace the clinician's analysis, but rather it provides an enhanced visualization of the EEG data (as demonstrated in our retrospective study) already being collected and analyzed in the clinical workflow. Finally, reimbursement risks will be mitigated if accurate identification of the EZ using the tool has the potential to significantly reduce or even eliminate the focal MRE segment reducing epilepsy-related costs by $6 billion/year. Consequently, healthcare and insurance providers will have a strong incentive to pay for, or reimburse epilepsy clinics for the tool.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
