EMAP: A Cloud-Edge Hybrid Framework for EEG Monitoring and Cross-Correlation Based Real-time Anomaly Prediction

Bharath Srinivas Prabakaran, Alberto Garcia Jimenez, Germán Moltó Martinez, and Muhammad Shafique. EMAP: A Cloud-Edge Hybrid Framework for EEG Monitoring and Cross-Correlation Based Real-time Anomaly Prediction. In 2020 57th ACM/IEEE Design Automation Conference (DAC), pp. 1–6, IEEE, 7 2020.

Download

[1.1MB pdf]  [HTML] 

Abstract

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.

BibTeX Entry

@inproceedings{Prabakaran2020,
   abstract = {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.},
   author = {Bharath Srinivas Prabakaran and Alberto Garcia Jimenez and Germán Moltó Martinez and Muhammad Shafique},
   doi = {10.1109/DAC18072.2020.9218713},
   isbn = {978-1-7281-1085-1},
   issue = {July},
   booktitle = {2020 57th ACM/IEEE Design Automation Conference (DAC)},
   month = {7},
   pages = {1-6},
   publisher = {IEEE},
   title = {EMAP: A Cloud-Edge Hybrid Framework for EEG Monitoring and Cross-Correlation Based Real-time Anomaly Prediction},
   url = {http://arxiv.org/abs/2004.10491 https://ieeexplore.ieee.org/document/9218713/},
   year = {2020}
}