EMAP: A cloud-edge hybrid framework for EEG monitoring and cross-correlation based real-time anomaly prediction

Bharath Srinivas Prabakaran, Alberto Garcia Jimenez, German Molto Martinez, Muhammad Shafique

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publication2020 57th ACM/IEEE Design Automation Conference, DAC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781450367257
DOIs
StatePublished - Jul 2020
Event57th ACM/IEEE Design Automation Conference, DAC 2020 - Virtual, San Francisco, United States
Duration: Jul 20 2020Jul 24 2020

Publication series

NameProceedings - Design Automation Conference
Volume2020-July
ISSN (Print)0738-100X

Conference

Conference57th ACM/IEEE Design Automation Conference, DAC 2020
CountryUnited States
CityVirtual, San Francisco
Period7/20/207/24/20

Keywords

  • Anomaly
  • Brain
  • Cloud
  • Edge
  • EEG
  • Electroencephalogram
  • Encephalopathy
  • Framework
  • IoT
  • Prediction
  • Seizure
  • Stroke
  • Wearable

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Modeling and Simulation

Fingerprint Dive into the research topics of 'EMAP: A cloud-edge hybrid framework for EEG monitoring and cross-correlation based real-time anomaly prediction'. Together they form a unique fingerprint.

Cite this