CSR: EAGER: Multi-physiological Signal Processing Architectures for Seizure Detection

CSR：EAGER：用于癫痫检测的多生理信号处理架构

• 批准号: 1350035
• 负责人: Tinoosh Mohsenin
• 金额: $ 9.98万
• 依托单位: University of Maryland Baltimore County
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-15 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1350035&HistoricalAwards=false
• 关键词: CSR; EAGER; Multi; physiological; Signal

The objective of this project is to overcome the limitations of sensor artifacts (noise), false detection, and energy/power constraints by combining the analysis of multiple physiological signals through specialized hardware which implements a multi-layer classification technique comprised of a unique sequence of signal processing and machine learning functions to distill time series data. The hypothesis is that a hybrid architecture can leverage common operations and communication patterns between DSP and machine learning to support these computations more efficiently than traditional digital signal processors and general purpose processors. This hypothesis is explored in the context of wearable seizure detection, using traces of EEG and other physiological sensor data obtained from the Epilepsy Center at University of Maryland Medical Center. Success of this exploratory research could have a significant impact for robust and efficient monitoring and use of continuous multi-physiological data for patients. Just in the context of epilepsy, it could enable seizure detection and caregiver alerts, which is important at night when seizures can happen without someone to help nearby. Longer term potential impacts extend to human-centered cyber-physical systems, cyber-security, and unmanned vehicles.

该项目的目标是通过专用硬件结合对多个生理信号的分析来克服传感器伪影（噪声）、错误检测和能量/功率限制的限制，该硬件实现了由独特的序列组成的多层分类技术。用于提取时间序列数据的信号处理和机器学习功能。假设混合架构可以利用 DSP 和机器学习之间的通用操作和通信模式来比传统数字信号处理器和通用处理器更有效地支持这些计算。 利用从马里兰大学医学中心癫痫中心获得的脑电图和其他生理传感器数据，在可穿戴癫痫检测的背景下探索了这一假设。这项探索性研究的成功可能会对患者连续多生理数据的稳健和有效监测和使用产生重大影响。仅在癫痫症的情况下，它可以实现癫痫发作检测和护理人员警报，这在夜间很重要，因为在没有人在附近帮助的情况下可能会发生癫痫发作。 更长期的潜在影响延伸到以人为中心的网络物理系统、网络安全和无人驾驶车辆。