论文标题
EMAP：用于EEG监测和基于互相关的实时异常预测的云边缘混合框架
EMAP: A Cloud-Edge Hybrid Framework for EEG Monitoring and Cross-Correlation Based Real-time Anomaly Prediction
论文作者
论文摘要
用于检测或预测神经系统疾病的最新技术（1）专注于单独预测每个疾病，并且（2）计算昂贵，导致延迟可能会使预测无用，尤其是在关键事件中。在此方面，我们提出了一个称为EMAP的实时两层框架，该框架在云中与我们的巨型数据库（多个EEG数据集的组合）中的所有EEG信号交叉相交，同时在边缘实时跟踪信号，以预测神经系统疾病的出现。使用所提出的框架，我们证明了我们测试的三种不同异常的预测准确性高达94％。
State-of-the-art techniques for detecting, or predicting, neurological disorders (1) focus on predicting each disorder individually, and are (2) computationally expensive, leading to a delay that can potentially render the prediction useless, especially in critical events. Towards this, we present a real-time two-tiered framework called EMAP, which cross-correlates the input with all the EEG signals in our mega-database (a combination of multiple EEG datasets) at the cloud, while tracking the signal in real-time at the edge, to predict the occurrence of a neurological anomaly. Using the proposed framework, we have demonstrated a prediction accuracy of up to 94% for the three different anomalies that we have tested.
