SBIR Phase I: Analog front end (AFE) platform for lightweight, long-term cortical monitoring

这个小型企业创新研究(SBIR)第一阶段项目的更广泛影响/商业潜力是展示智能可穿戴脑电(EEG)监测和分析平台作为远程癫痫检测系统的可行性。癫痫是一种神经系统疾病，影响着全球5000万各个年龄段的人。美国有300多万人受到影响，每年新增确诊病例15万例。然而，确定和确定癫痫发作的类型可能代价高昂。脑电记录大脑的电活动，可用于癫痫诊断。今天，这项测试在医院进行，患者被连续监测几天。该项目将实现端到端系统开发，开发一种坚固、轻便、无线的脑电图帽，将信号远程传输到服务器，服务器将生成报告，供医生分析患者的神经数据进行治疗。拟议的技术是一种长期的、轻型的动态癫痫监测系统，具有以下优点：1)患者不再需要忍受长时间的住院治疗；2)可以在临床环境之外检测到更多的癫痫发作；3)降低了持续监测的成本。小型企业创新研究(SBIR)第一阶段项目将：(1)证明与基准相比，拟议的模拟前端(AFE)在EEG频段(1-100赫兹)具有最高的噪声功率效率；(2)制定系统功率预算，以优化低功率环境下的信号处理；以及(3)对误差源(电子噪声、电源干扰等)进行信号完整性分析。以优化性能。AFE是拟议的便携式脑电图仪(EEG)设备的基础，该设备用于临床、学术和研究团体的神经学数据采集。该系统将把目前兴起的数字健康设备扩展到神经状态的复杂环境中，并推进新的神经分析系统。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 Innovation Research (SBIR) Phase I project is to demonstrate the feasibility of an intelligent, wearable electroencephalogram (EEG) monitoring and analytics platform as a remote seizure detection system. Epilepsy is a neurological disorder affecting 50 million people of all ages globally. Over 3 million people in the U.S. are affected with 150,000 new cases diagnosed each year. However, identifying and characterizing the type of seizure can be costly. EEGs record the electrical activity of the brain and can be used for epilepsy diagnosis. Today, this test is administered in a hospital setting and the patient is monitored continuously for several days. This project will enable the end-to-end system development of a robust, lightweight, wireless EEG cap to transmit signals remotely to a server, which will generate reports for the physician to analyze a patient neurological data for treatment. The proposed technology is a long-term, lightweight ambulatory seizure monitoring system offering advantages, including 1) Patients no longer have to endure long hospital stays, 2) More seizure instances can be detected outside clinical settings, and 3) Costs of ongoing monitoring are lowered. This Small Business Innovation Research (SBIR) Phase I project will: (1) demonstrate that a proposed Analog Front End (AFE) is the most noise-power efficient in the EEG band (1-100 Hz) compared to benchmarks; (2) develop a system power budget to optimize signal processing in a low-power environment; and (3) perform signal integrity analysis of error sources (electronic noise, mains power interference, etc.) to optimize performance. The AFE is the foundation of a proposed portable electroencephalograph (EEG) device for neurological data acquisition for the clinical, academic, and research communities. This system will extend the current rise of digital health devices into the complex environment of neurological states, as well as advancing new neuro-analytic systems.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.