Biosensor Data Fusion for Real-Time Monitoring of Global Neurophysiological Function

实时检测神经生理状态的急性变化，如癫痫发作、认知能力衰退、急性应激等，最终可以防止需要坚定不移专注的高危职业中的事故发生。这类职业包括危险货物卡车运输、重型机械操作、安全和国防、空中交通管制等。事实上，用于获取丰富的生物传感器数据流的技术正在变得越来越便携和非侵入性，这些数据流捕捉大脑功能，例如脑电。这些发展不仅提供了实现实时监控的机会，而且提供了可用于指示生理状态退化的先发制人警报(例如，智能电话显示器)。这项研究在生物医学环境中有直接的应用--例如，癫痫是最常见的神经疾病之一，困扰着全球5000多万人，其中包括美国的300万人。在这些患者中，约25%的患者癫痫发作无法通过现有药物控制。能够检测(或预测)癫痫发作的发作将显著提高患者的生活质量。在一项概念验证研究中，研究小组的新分析方法在2.5秒内检测到癫痫发作的发作。相比之下，现有方法的检测延迟超过7秒。从更广泛的角度来看，本研究的成果可以改变神经生理功能实时监测的现状。多学科研究团队将努力为不同的学生群体提供最先进的研究和培训机会，弥合从工程学到生命科学和脑科学的差距。该研究小组将开发一种基于图论拓扑映射的传感器数据融合方法，将从多个生物传感器获得的数据组合在一起，用于神经生理变化点检测。与依赖于复杂信号预处理的现有方法不同，图论方法避免了这些计算量大的步骤，因此在实际环境中更可行。研究小组将使用从终端用户那里获得的高分辨率神经生理记录的数据库来利用这一框架，这些记录在现实环境中会导致全球功能状态的变化(例如，急性应激、认知衰竭和疲劳等)。研究团队将在整个方案中集成自动化决策方法，以合成信息并向最终用户提供易于解释的反馈(例如，在智能设备上显示)。此外，PI将定制生物传感器以适应患者的生活方式。

Real-time detection of acute changes in neurophysiological state, such as epileptic seizures, lapses in cognitive ability, acute stress, etc., can ultimately serve to prevent accidents in high-risk occupations that require unwavering focus. Such professions include hazardous cargo trucking, heavy machinery operation, security and defense, air traffic control, etc. Indeed, technology for acquiring rich biosensor data streams that capture brain function, e.g., electroencephalography, are becoming increasingly portable and noninvasive. These developments present an opportunity for implementing not only real-time monitoring, but also providing pre-emptive alerts (e.g., smart phone displays), which can be used to indicate degradation in physiological states. This research has direct applications in biomedical settings - for instance, epilepsy, is one of the most common neurological disorders afflicting over 50 million people worldwide, including 3 million people in the U.S. In about 25 percent of these patients, epileptic seizures are not controlled using available medications. Being able to detect (or predict) the onset of epileptic seizures would significantly enhance the patient's quality of life. In a proof-of-concept study, the novel analytical approaches by the research team detected the onset of epileptic seizures within 2.5 seconds. In contrast, existing approaches have a detection delay exceeding 7 seconds. From a broader perspective, the findings of this research can transform the status quo in real-time monitoring of neurophysiological function. The multidisciplinary research team will strive to provide state-of-the-art research and training opportunities for a diverse group of students that bridges the gap from engineering to the life and brain sciences. The research team will develop a sensor data fusion approach based on graph theoretic topological mapping to combine data acquired from multiple biosensors for neurophysiological change point detection. Unlike existing approaches, which rely on complex signal pre-processing, the graph theoretic approach eschews these computationally demanding steps and is therefore more viable in a practical setting. The research team will exploit this framework using a data library of high-resolution neurophysiological recordings acquired from end users in realistic settings that induce shifts in global functional states (e.g., acute stress, cognitive exhaustion, and fatigue and so on). The research team will integrate automated decision-making approaches in the overall schema to synthesize the information and provide easily interpretable feedback to the end user (e.g., displays on a smart device). Furthermore, the PIs will customize biosensors to accommodate the patient's lifestyle.