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Neurologic Solutions, Inc.

Address

1836 Birch Rd
McLean, VA, 22101-5252
USA

UEI: RDKKFS37GUL1

Number of Employees: 2

HUBZone Owned: No

Woman Owned: Yes

Socially and Economically Disadvantaged: No

SBIR/STTR Involvement

Year of first award: 2018

Phase I Awards

Phase II Awards

50%

Conversion Rate

$481,000

Phase I Dollars

$1,204,564

Phase II Dollars

$1,685,564

Total Awarded

Awards

Up to 10 of the most recent awards are being displayed. To view all of this company's awards, visit the Award Data search page.

EpiScalp: An EEG Analytics Solution to Improve Diagnosis of Epilepsy

Amount: $1,204,564 Topic: 106

Project Summary According to the WHO, an estimated 5 million people worldwide receive a diagnosis of epilepsy annually. Unfortunately, misdiagnosis rates range between 20% to 42%. A false positive leads to inappropriate treatment with unnecessary antiseizure medication with potential adverse reactions, failure to receive suitable therapy for the correct diagnosis, and unnecessary restrictions that arise with the stigma of epilepsy. A false negative comes with increased risks of seizure recurrence, status epilepticus, and premature death. The diagnosis of epilepsy depends on a comprehensive clinical history, neurological examination, and ancillary studies including scalp electroencephalography (EEG). Scalp EEG can confirm an epilepsy diagnosis if abnormalities indicating epilepsy, such as random interictal (between seizure) epileptiform discharges (IEDs) or focal slow wave activity, are present and detected by visual inspection. However, the sensitivity of abnormalities being present in the EEG varies from 29-55%, and the ability for clinicians or EEG technicians to detect them by visual inspection varies. EEG artifacts can both mask IEDs and be mistaken for IEDs. Consequently, it takes multiple visits, months, or even years to be accurately diagnosed. We propose to further develop and validate EpiScalp, a revolutionary EEG analytics algorithm to enhance diagnostic accuracy. EpiScalp produces a risk score between 0-1 from 10-20 minutes of EEG data and does not rely on the presence of EEG abnormalities. Instead, our novel algorithm predicts epilepsy in resting-state (no seizure) brain networks using a dynamic network model. In Phase 1, EpiScalp underwent evaluation on 198 patients with EEGs void of abnormalities during their first visits. An alarming 54% (107) of these patients were misdiagnosed as having epilepsy when they did not. EpiScalp achieved definitive diagnoses for 168 of 198 patients with low (epilepsy unlikely) and very (epilepsy likely) risk scores, demonstrating remarkable accuracy, sensitivity, and specificity at 93%, 92%, and 95% respectively. EpiScalp could have reduced misdiagnoses from 54% to 17% (a 69% reduction). EpiScalp's predictive capabilities extend to patients undergoing long-term EEG monitoring within the epilepsy monitoring unit (EMU). Notably, 30-50% of EMU beds are occupied by non-epileptic patients, resulting in care delays for those requiring admission due to seizure exacerbation or medically refractory epilepsy needing surgical evaluation. This issue significantly impacts center efficiency and care quality. Through risk-based patient triage, EpiScalp improves care quality for epilepsy patients in need and empowers centers to efficiently assess more potential surgical candidates. This optimization of EMU resources enables timelier treatment for patients. In Phase 2, we aim to validate EpiScalp through prospective observational studies involving first visit and EMU patients at 3 renowned epilepsy centers in the US. Additionally, we will assess usability of our tool and develop a comprehensive regulatory and rollout plan in partnership with experts from DIXI Medical.

SBIR

Phase II

2024

HHS

NIH

SBIR Phase I: Improving Diagnosis of Epilepsy by Applying Network Analytics to Non-Seizure Scalp EEG Data

Amount: $256,000 Topic: BM

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is the development of a novel electroencephalogram (EEG) analytics tool that will improve the speed and accuracy of diagnosing epilepsy. The tool is an easy-to-use software package that utilizes scalp EEG data. It is being developed as a cloud-based application designed to integrate with existing software packages and to provide easy-to-read heatmaps available within minutes. Epilepsy centers and other settings where EEG diagnostics are used will benefit from improved accuracy in diagnosing epilepsy: Currently the accuracy is estimated at less than 60%, whereas the proposed tool can improve this figure by over 25%, more accurately distinguishing between epileptic and non-epileptic pathologies from EEG alone. Furthermore, the technology will increase the speed of epilepsy diagnosis: Currently, patients often require multiple EEGs, during which they are at high risk of further seizures. The proposed tool will provide a definitive diagnostic on the first visit. This Small Business Innovation Research (SBIR) Phase I project involves performing a retrospective study to validate a novel EEG analytics tool on 60 or more patients, developing an algorithm to automate artifact removal from scalp EEG data most appropriate for this clinical application, and developing the tool as a cloud-based service. These milestones will facilitate clinical adoption and easy integration into the clinical workflow, both of which are necessary for successful commercialization of the innovation. The tool will predict if a brain network is epileptic while a patient is monitored at rest when no seizure occurs. The key strengths are the use of a dynamic network model (DNM) to uncover connections in the brain that only exist in an epilepsy patient during rest. All other FDA proved tools are based on individual EEG channel properties rather than network-based properties. As a result, their utility is limited to identifying abnormal events (e.g., when an EEG spike occurs), potentially vulnerable to artifacts. In addition, the proposed tool is transformative because it captures how nodes in a network dynamically influence each other, while clinical approaches rely on reading EEG with naked eyes. 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.

SBIR

Phase I

2021

NSF

SBIR Phase I: A Novel Analytical Tool to Localize the Epileptogenic Zone in Medically-Refractory Epilepsy

Amount: $225,000 Topic: BM

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,

SBIR

Phase I

2018

NSF