Characterization of sleep spindles using higher order statistics and spectra

Tayfun Akgul, Mingui Sun, Robert J. Sclabassi, A. Enis Cetin

Research output: Contribution to journalArticlepeer-review

40 Citations (Scopus)

Abstract

This work characterizes the dynamics of sleep spindles, observed in electroencephalogram (EEG) recorded from humans during sleep, using both time and frequency domain methods which depend on higher order statistics and spectra. The time domain method combines the use of second- and third-order correlations to reveal information on the stationarity of periodic spindle rhythms to detect transitions between multiple activities. The frequency domain method, based on normalized spectrum and bispectrum, describes frequency interactions associated with nonlinearities occurring in the observed EEG.

Original languageEnglish
Pages (from-to)997-1009
Number of pages13
JournalIEEE Transactions on Biomedical Engineering
Volume47
Issue number8
DOIs
Publication statusPublished - Aug 2000
Externally publishedYes

Funding

Manuscript received November 26, 1998; revised March 10, 2000. The work of T. Akgül was supported in part by TÜBİTAK-BAYG and Çukurova University, Turkey. The work of the M. Sun and R. J. Sclabassi was supported in part by the Whitaker Foundation and in part by the National Institutes of Health. Asterisk indicates corresponding author. *T. Akgül is with TUBITAK Marmara Research Center, Information Technologies Research Institute, 41470 Gebze-Kocaeli, Turkey.

FundersFunder number
TÜBİTAK-BAYG
National Institutes of Health
Whitaker Foundation
Çukurova Üniversitesi

    Keywords

    • Bispectrum
    • Cumulants
    • EEG
    • Higher order spectra
    • Higher order statistics
    • Sleep spindles
    • Sum-of-cumulants

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