Please use this identifier to cite or link to this item: https://idr.l1.nitk.ac.in/jspui/handle/123456789/16627
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dc.contributor.authorGopan
dc.contributor.authorGopika K.; Prabhu
dc.contributor.authorSathvik S.; Sinha
dc.contributor.authorNeelam
dc.date.accessioned2021-05-05T10:31:05Z-
dc.date.available2021-05-05T10:31:05Z-
dc.date.issued2020
dc.identifier.citationBIOCYBERNETICS AND BIOMEDICAL ENGINEERING Vol. 40 , 1 , p. 527 - 545en_US
dc.identifier.urihttps://doi.org/10.1016/j.bbe.2020.01.013
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/16627-
dc.description.abstractBackground: Sleep is vital for normal body functions as sleep disorders can adversely affect a person. Electroencephalographic (EEG) signals indicate brain functions and have characteristic signatures for various sleep stages. These enable the use of EEG as an effective tool for in-depth studies about sleep. Sleep stages are broadly divided as rapid eye movement (REM) and non-rapid eye movement (NREM). NREM is further divided into 3 stages. The objective of the work is to distinguish the given EEG epoch as wake, NREM1, NREM2, NREM3 and REM. DREAMS Subject Database containing 5 EEG channels is used here. This work focuses on utilizing EEG by exploiting variations in inter-dependencies of different brain regions during sleep. New method: Covariance matrices of the wavelet-decomposed channels are used to obtain the variations in inter-dependencies. The feature sets are: (1) simple matrix properties(MF) like trace, determinant and norm, (2) eigen-values (E1), (3) eigen- vector corresponding to the largest eigen-value (E2) and (4) tangent vectors obtained using Riemann geometry (RG-TS). The features are input to ensemble classifier with bagging. Subject-specific, All-subjects-combined and Leave-one-subject-out methods of analysis are carried out. Results: In all methods of analysis, RG-TS features give maximum accuracy (80.05%, 83.05% and 61.79%), closely followed by E1 (79.49%, 77.14% and 58.34%). Comparison with existing method: The proposed method obtains higher and/or comparable accuracy. This work also ensures no biasing of classifier due to unequal class distribution. Conclusion: The performances of RG-TS and E1 features reveal that the changes in interdependencies of pre-frontal and occipital lobe along with the central lobe can be used to distinguish the different sleep stages. (c) 2020 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.en_US
dc.titleSleep EEG analysis utilizing inter-channel covariance matricesen_US
dc.typeArticleen_US
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