title: Independent component analysis as a preprocessing step for data compression of neonatal EEG.
abstract: We propose a novel approach for compressive sampling of the neonatal electro-encefalogram (EEG) data. The method assumes that the set of EEG data is generated by linearly mixing a fewer number of source signals. Another assumption is that the sources are nearly-sparse in Gabor dictionary. The presented method, instead of compressing original EEG channels, first performs a data-reduction, and then compresses the obtained sources. With this approach we showed that the gain in reconstruction speed is 33%-50%, whereas the compression rate is enhanced by 33%.
authors.affiliation: Department of Electrical Engineering, ESAT-SCD, Katholieke Universiteit Leuven, Belgium. bogdan.mijovic@esat.kuleuven.be
authors.forename: Bogdan
authors.initials: B
authors.lastname: Mijovíc
authors.forename: Vladimir
authors.initials: V
authors.lastname: Matić
authors.forename: Maarten
authors.initials: M
authors.lastname: De Vos
authors.forename: Sabine
authors.initials: S
authors.lastname: Van Huffel
mesh_terms: D000465:Algorithms; D002309:Cardiology / Q000379:methods; D003198:Computer Simulation; D044962:Data Compression / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D002534:Hypoxia, Brain / Q000175:diagnosis* / Q000473:pathology; D007231:Infant, Newborn; D015931:Intensive Care, Neonatal / Q000379:methods*; D015233:Models, Statistical; D008991:Monitoring, Physiologic / Q000295:instrumentation* / Q000379:methods; D016011:Normal Distribution; D011336:Probability; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors