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

该项目的目标是通过专用硬件结合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/功率约束的限制，该专用硬件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，以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。 在可穿戴癫痫发作检测的背景下，使用从马里兰州医学中心癫痫中心获得的EEG和其他生理传感器数据的痕迹来探索该假设。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。仅仅在癫痫的背景下，它可以实现癫痫发作检测和护理人员警报，这在夜间癫痫发作时很重要，因为附近没有人帮助。 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汽车。

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.