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.
@article{arxiv.2004.10491,
title = {EMAP: A Cloud-Edge Hybrid Framework for EEG Monitoring and Cross-Correlation Based Real-time Anomaly Prediction},
author = {Bharath Srinivas Prabakaran and Alberto García Jiménez and Germán Moltó Martínez and Muhammad Shafique},
journal= {arXiv preprint arXiv:2004.10491},
year = {2020}
}
Comments
Accepted for Publication at the 57th Design Automation Conference (DAC), July 2020, San Francisco, CA, USA