Classification of sleep stages using multi-wavelet time frequency entropy and LDA

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dc.contributor.authorLweesy, Khaldon
dc.contributor.authorFraiwan, Luay
dc.contributor.authorKhasawneh, Natheer
dc.contributor.authorETAL..
dc.date.accessioned2023-12-15T06:17:40Z
dc.date.available2023-12-15T06:17:40Z
dc.date.issued2010
dc.description.abstractThe process of automatic sleep stage scoring consists of two major parts: feature extraction and classification. Features are normally extracted from the polysomno-graphic recordings, mainly electroencephalograph (EEG) signals. The EEG is considered a non-stationary signal which increases the complexity of the detection of different waves in it. Keywords: Sleep stage scoring, Multi-wavelets, Time-frequency entropy, Linear discriminant analysis
dc.identifier.citationFraiwan, L., Lweesy, K., Khasawneh, N., Fraiwan, M., Wenz, H., & Dickhaus, H. (2010). Classification of sleep stages using multi-wavelet time frequency entropy and LDA. Methods of information in Medicine, 49(03), 230-237.
dc.identifier.doihttps://doi.org/10.3414/ME09-01-0054
dc.identifier.urihttps://dspace.adu.ac.ae/handle/1/241
dc.language.isoen
dc.publisherThieme
dc.titleClassification of sleep stages using multi-wavelet time frequency entropy and LDA
dc.typeArticle

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