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

生物传感器数据融合实时监测整体神经生理功能

• 批准号: 1719388
• 负责人: Prahalada Rao
• 金额: $ 21.3万
• 依托单位: University of Nebraska-Lincoln
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-10-21 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1719388&HistoricalAwards=false
• 关键词: Biosensor; Data; Fusion; Real; Time

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.

实时检测神经生理状态的急性变化，如癫痫发作、认知能力丧失、急性应激等，最终可以防止高风险职业中的事故，这些职业需要坚定不移的关注。这些专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。实际上，用于获取捕获大脑功能的丰富生物传感器数据流的技术，例如，脑电图，正变得越来越便携和非侵入性。这些发展不仅提供了实施实时监控的机会，而且还提供了先发制人的警报（例如，智能电话显示器），其可用于指示生理状态的退化。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系统疾病之一，困扰着全世界超过5000万人，其中包括美国的300万人。能够检测（或预测）癫痫发作的发作将显着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。相比之下，现有方法具有超过7秒的检测延迟。从更广泛的角度来看，这项研究的发现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群体的学生提供最先进的研究和培训机会，弥合从工程到生命和脑科学的差距。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，以联合收割机结合从多个生物传感器获取的数据，用于神经生理学变化点检测。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要求高的步骤，因此在实际环境中更可行。研究小组将利用这一框架，使用从最终用户获得的高分辨率神经生理记录的数据库，在现实环境中诱导全局功能状态的变化（例如，急性应激、认知衰竭和疲劳等）。研究团队将在总体方案中集成自动化决策方法，以综合信息并向最终用户提供易于解释的反馈（例如，显示在智能设备上）。此外，PI将定制生物传感器以适应患者的生活方式。

项目成果

Paired Trial Classification: A Novel Deep Learning Technique for MVPA

• DOI: 10.3389/fnins.2020.00417
• 发表时间: 2020-04
• 期刊: Frontiers in Neuroscience
• 影响因子: 4.3
• 作者: Jacob M. Williams;A. Samal;Prahalada K. Rao;Matthew R. Johnson

The role of low-level image features in the affective categorization of rapidly presented scenes

• DOI: 10.1371/journal.pone.0215975
• 发表时间: 2019-05
• 期刊: PLoS ONE
• 影响因子: 3.7
• 作者: L. Jack Rhodes;Matthew Ríos;Jacob M. Williams;Gonzalo Quiñones;Prahalada K. Rao;V. Miskovic