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

这个小型企业创新研究(SBIR)第一阶段项目的更广泛的影响/商业潜力是开发一种新的脑电(EEG)分析工具，该工具将提高诊断癫痫的速度和准确性。该工具是一个使用头皮脑电数据的简单易用的软件包。它被开发为一个基于云的应用程序，旨在与现有的软件包集成，并在几分钟内提供易于阅读的热图。使用脑电诊断的癫痫中心和其他场所将受益于癫痫诊断准确率的提高：目前，准确率估计不到60%，而建议的工具可以将这一数字提高25%以上，更准确地区分癫痫和非癫痫病理。此外，该技术将提高癫痫诊断的速度：目前，患者经常需要多个脑电图，在此期间，他们面临着进一步癫痫发作的高风险。建议的工具将在第一次访问时提供明确的诊断。该小型企业创新研究(SBIR)第一阶段项目包括进行一项回溯性研究，以在60名或更多患者身上验证一种新的脑电分析工具，开发一种算法以自动从最适合该临床应用的头皮脑电数据中去除伪影，并将该工具开发为基于云的服务。这些里程碑将促进临床采用和轻松整合到临床工作流程中，这两者都是创新成功商业化所必需的。该工具将预测大脑网络是否患有癫痫，而当患者没有癫痫发作时，则会在休息时进行监测。关键的优势是使用动态网络模型(DNM)来发现大脑中只有癫痫患者在休息时才存在的连接。FDA证明的所有其他工具都是基于单个EEG通道属性，而不是基于网络的属性。因此，它们的用途仅限于识别异常事件(例如，当EEG尖峰出现时)，可能容易受到伪影的影响。此外，拟议的工具具有变革性，因为它捕获了网络中的节点如何动态相互影响，而临床方法依赖于肉眼读取EEG。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.