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

该项目的目标是克服传感器伪像（噪声）、误检和能量/功率限制的限制，通过专门的硬件结合多种生理信号的分析，实现由独特的信号处理序列和机器学习功能组成的多层分类技术，以提取时间序列数据。假设是混合架构可以利用DSP和机器学习之间的通用操作和通信模式，比传统的数字信号处理器和通用处理器更有效地支持这些计算。这一假设是在可穿戴癫痫检测的背景下进行的，使用了从马里兰大学医学中心癫痫中心获得的脑电图痕迹和其他生理传感器数据。这项探索性研究的成功将对稳健有效的监测和患者连续多生理数据的使用产生重大影响。就癫痫而言，它可以实现癫痫发作检测和护理警报，这在夜间很重要，因为夜间癫痫发作时附近没有人帮助。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。

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.