STTR Phase I: Patient-Specific System for Early Detection and Identification of Epileptic Seizures

这个小企业技术转让(STTR)第一阶段项目的更广泛的影响/商业潜力是为癫痫患者及其照顾者提供一个智能系统，可以在癫痫发作之前预测发作。美国有300多万成年人和100多万儿童，全世界有5000多万人患有癫痫。反复和不可预测的癫痫发作严重影响癫痫患者的生活质量。这些癫痫发作仍然是癫痫患者及其照顾者经济、情感和身体伤害的主要原因。设计、开发和集成人工智能(AI)模型与检测脑波异常的仪器，如脑电(EEG)，用于实时癫痫发作预测，可能会为这些患者及其照顾者带来改善。这项技术将占领美国快速增长的60亿美元人工智能医疗解决方案市场的一部分。这个小企业技术转让(STTR)第一阶段项目支持开发一种新型消费产品，与照顾者合作，主动降低癫痫患者发作事件的风险。目前的商业解决方案大多是反应性的，只有在癫痫发作后才能获得支持。该公司将通过开发、测试、集成和评估应用于脑电数据的机器学习(ML)模型来填补这一空白，用于癫痫发作预测。科学方法将利用内在的异质和复杂的边缘技术。与第三方供应商EEG帽、微控制器、智能手机和云服务的数据连接依赖于许多不同的运营技术和通信标准。这项研究将通过硬件和软件解决方案克服这些挑战，这些解决方案将在边缘设备中集成这些服务，以实现应用程序可移植性并简化部署。将使用强大的交叉验证技术、广泛的测试和使用社区标准的基准来解决诸如对有限的计算能力和能源设备的推断及其对预测准确性/敏感性的影响等挑战。这项研究的技术产品将提高照顾者的知识，增加对癫痫发作的了解，并增加患者的福祉。该奖项反映了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 Technology Transfer (STTR) Phase I project is to provide epileptic patients, and their caregivers a smart system that can predict seizures before they occur. There are more than 3 million adults and 1 million children in the US, and more than 50 million people worldwide, suffering from epilepsy. Repeated and unpredictable seizures significantly affect the quality of life of people suffering from epilepsy. These seizures remain the leading cause of economic, emotional, and physical injuries for people with epilepsy and their caregivers. Design, development, and integration of artificial intelligence (AI) models with instruments that detect abnormalities in brain waves like electroencephalogram (EEG) for real-time seizure prediction may bring improvements for these patients and their caregivers. This technology is poised to capture a portion of the rapidly growing $6 billion US market of AI healthcare solutions.This Small Business Technology Transfer (STTR) Phase I project supports the development of a novel consumer product that works with caregivers to proactively mitigate the risk of seizure events in people with epilepsy. Current commercial solutions are mostly reactive, and support is available only after a seizure event. The company will fill this gap by developing, testing, integrating, and evaluating machine learning (ML) models - applied to EEG data - for epileptic seizure prediction. The scientific approach will leverage inherently heterogenous and complex edge technologies. Data connectivity with third party vendor EEG caps, microcontrollers, smart phones, and cloud services rely on many different operational technologies and communication standards. This research will overcome these challenges with hardware and software solutions that will integrate these services within an edge device to enable application portability and simplify deployment. Challenges such as inference on limited computational power and energy devices, and its effects on the accuracy/sensitivity of the predictions will be solved using robust cross-validation techniques, extensive testing, and benchmarking using community standards. The technical product of this research will advance caregiver knowledge and increase understanding of epileptic seizures as well as increase patient well-being.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.