Title :
Adaptive spectral analysis of sleep spindles based on subspace tracking
Author :
Caspary, O. ; Nus, P.
Author_Institution :
Centre de Recherche en Autom. de Nancy, CNRS, Saint-Die, France
fDate :
31 Oct-3 Nov 1996
Abstract :
A method to track the spectra of human sleep electroencephalogram (EEG) spindles is presented. This method uses a low-rank approximation of the covariance matrix and offers a compromise between numerical complexity and convergence. In the first part of the article, the authors describe the method briefly. In the second part, they apply it to filtered spindles to find an adequate agreement with a model of spindles that they put forward. Finally, it is concluded that there are different sorts of spindles according to frequency variation
Keywords :
adaptive signal processing; electroencephalography; medical signal processing; spectral analysis; EEG analysis; adaptive spectral analysis; convergence; covariance matrix; filtered spindles; frequency variation; low-rank approximation; numerical complexity; sleep spindles; spectra tracking method; subspace tracking; Convergence of numerical methods; Covariance matrix; Electroencephalography; Equations; Frequency estimation; Humans; Matrix decomposition; Signal to noise ratio; Sleep; Spectral analysis;
Conference_Titel :
Engineering in Medicine and Biology Society, 1996. Bridging Disciplines for Biomedicine. Proceedings of the 18th Annual International Conference of the IEEE
Conference_Location :
Amsterdam
Print_ISBN :
0-7803-3811-1
DOI :
10.1109/IEMBS.1996.652668
