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AuthorHussein R.
AuthorMohamed A.
AuthorAlghoniemy M.
AuthorAwad A.
Available date2022-04-21T08:58:35Z
Publication Date2013
Publication Name2013 4th Annual International Conference on Energy Aware Computing Systems and Applications, ICEAC 2013
ResourceScopus
Identifierhttp://dx.doi.org/10.1109/ICEAC.2013.6737653
URIhttp://hdl.handle.net/10576/30166
AbstractEpileptic detection techniques rely heavily on the Electroencephalography (EEG) as a representative signal carrying valuable information pertaining to the current brain state. In this work, we investigate the stability of time domain EEG features while varying the channel conditions. We identify the feature sets that would provide the most robust EEG classification accuracy. Moreover, an embedded Compressive Sensing (CS)-based EEG encoding system whose complexity is adapted to the channel condition is proposed. We also propose a framework called Classification Accuracy-Compression Ratio-Signal to Noise Ratio (CA-CR-SNR) that adapts compression ratio according to the channel condition. Simulation results show that selecting appropriate EEG feature combinations can relatively overcome the impact of bad channel conditions; however, this simple solution is still inadequate. The proposed adaptive algorithm reconfigures the compression ratio based on a channel feedback signal to further improve the classification accuracy. 2013 IEEE.
SponsorQatar National Research Fund
Languageen
PublisherIEEE Computer Society
SubjectAdaptive algorithms
Compression ratio (machinery)
Electrophysiology
Feature extraction
Neurodegenerative diseases
Neurophysiology
Signal detection
Signal reconstruction
Classification accuracy
Compressive sensing
Design and analysis
EEG signals
Epileptic detection
Epileptic seizure detection
Epileptic seizures
Feature combination
Signal to noise ratio
TitleDesign and analysis of an adaptive compressive sensing architecture for epileptic seizure detection
TypeConference Paper
Pagination141-146
dc.accessType Abstract Only


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