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AuthorSamiee K.
AuthorKovacs P.
AuthorKiranyaz, Mustafa Serkan
AuthorGabbouj M.
AuthorSaramaki T.
Available date2022-04-26T12:31:23Z
Publication Date2015
Publication Name2015 23rd European Signal Processing Conference, EUSIPCO 2015
ResourceScopus
Identifierhttp://dx.doi.org/10.1109/EUSIPCO.2015.7362706
URIhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84963977864&doi=10.1109%2fEUSIPCO.2015.7362706&partnerID=40&md5=acc0110e3503feee00e280f2bad689f3
URIhttp://hdl.handle.net/10576/30634
AbstractA sparse representation of ID signals is proposed based on time-frequency analysis using Generalized Rational Discrete Short Time Fourier Transform (RDSTFT). First, the signal is decomposed into a set of frequency sub-bands using poles and coefficients of the RDSTFT spectra. Then, the sparsity is obtained by applying the Basis Pursuit (BP) algorithm on these frequency sub-bands. Finally, the total energy of each subband was used to extract features for offline patient-specific sleep stage classification of single channel EEG records. In classification of over 670 hours sleep Electroencephalography of 39 subjects, the overall accuracy of 92.50% on the test set is achieved using random forests (RF) classifier trained on 25% of each sleep record. A comparison with the results of other state-of-art methods demonstrates the effectiveness of the proposed sparse decomposition method in EEG signal analysis.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectBiomedical signal processing
Classification (of information)
Decision trees
Electroencephalography
Electrophysiology
Rational functions
Sleep research
basis pursuit
Discrete short time fourier transforms
Sleep stage
sleep-EDF
Sparse decomposition
Sparse representation
sparsity
Time frequency analysis
Signal processing
TitleSleep stage classification using sparse rational decomposition of single channel EEG records
TypeConference Paper
Pagination1860-1864
dc.accessType Abstract Only


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