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

Volume

3

fYear

1996

fDate

31 Oct-3 Nov 1996

Firstpage

976

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/IEMBS.1996.652668

Filename

652668