TY - GEN
T1 - EMAP
T2 - 57th ACM/IEEE Design Automation Conference, DAC 2020
AU - Prabakaran, Bharath Srinivas
AU - Garcia Jimenez, Alberto
AU - Martinez, German Molto
AU - Shafique, Muhammad
N1 - Funding Information:
signals and predict the occurrence of an anomaly. Using the proposed, we have achieved a prediction accuracy of 94%, 73%, and 79% for three differentanomalies, namely, seizures, encephalopathy, and strokes, respectively. The EMAP framework has been made open-source at https: //emap.sourceforge.io, to ensure ease of adoption and reproducibilit.y ACKNOWLEDGEMENT This work was partially supported by Doctoral College Resilient Embedded Systems which is run jointly by TU Wien’s Faculty of Informatics and FH-TechnikumWien.
Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/7
Y1 - 2020/7
N2 - 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.
AB - 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.
KW - Anomaly
KW - Brain
KW - Cloud
KW - Edge
KW - EEG
KW - Electroencephalogram
KW - Encephalopathy
KW - Framework
KW - IoT
KW - Prediction
KW - Seizure
KW - Stroke
KW - Wearable
UR - http://www.scopus.com/inward/record.url?scp=85093936433&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85093936433&partnerID=8YFLogxK
U2 - 10.1109/DAC18072.2020.9218713
DO - 10.1109/DAC18072.2020.9218713
M3 - Conference contribution
AN - SCOPUS:85093936433
T3 - Proceedings - Design Automation Conference
BT - 2020 57th ACM/IEEE Design Automation Conference, DAC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 20 July 2020 through 24 July 2020
ER -