Literature DB >> 16497384

Determination of dominant simulated spindle frequency with different methods.

Eero Huupponen1, Wim De Clercq, Germán Gómez-Herrero, Antti Saastamoinen, Karen Egiazarian, Alpo Värri, Bart Vanrumste, Anneleen Vergult, Sabine Van Huffel, Wim Van Paesschen, Joel Hasan, Sari-Leena Himanen.   

Abstract

Accurate analysis of EEG sleep spindle frequency is challenging. The frequency content of true sleep spindles is not known. Therefore, simulated spindle activity was studied in the present work. Five types of simulated test signals were designed, all containing a dominant spindle represented by a 13-Hz sine wave as such or with a waxing and waning pattern accompanied by a secondary spindle activity in three test signals. Background EEG was included in four test signals, modeled either as small additional sinusoids across the spindle frequency range or as filtered Gaussian noise segments. The purpose of this study was to investigate how accurately the dominant spindle frequency of 13 Hz could be resolved with different methods in the presence of the interfering waveforms. A matching pursuit (MP) based approach, discrete Fourier transform (DFT) with Hanning windowing with and without zero padding, Hankel total least squares (HTLS) and wavelet methods were compared in the analyses. MP method provided best overall performance, followed closely by DFT with zero padding. Comparative studies like this are important to decide the method of choice in clinical sleep EEG analysis.

Mesh:

Year:  2006        PMID: 16497384     DOI: 10.1016/j.jneumeth.2006.01.013

Source DB:  PubMed          Journal:  J Neurosci Methods        ISSN: 0165-0270            Impact factor:   2.390


  3 in total

Review 1.  Sleep Spindles as an Electrographic Element: Description and Automatic Detection Methods.

Authors:  Dorothée Coppieters 't Wallant; Pierre Maquet; Christophe Phillips
Journal:  Neural Plast       Date:  2016-07-11       Impact factor: 3.599

2.  Expert and crowd-sourced validation of an individualized sleep spindle detection method employing complex demodulation and individualized normalization.

Authors:  Laura B Ray; Stéphane Sockeel; Melissa Soon; Arnaud Bore; Ayako Myhr; Bobby Stojanoski; Rhodri Cusack; Adrian M Owen; Julien Doyon; Stuart M Fogel
Journal:  Front Hum Neurosci       Date:  2015-09-24       Impact factor: 3.169

3.  Automatic Sleep Spindle Detection and Genetic Influence Estimation Using Continuous Wavelet Transform.

Authors:  Marek Adamczyk; Lisa Genzel; Martin Dresler; Axel Steiger; Elisabeth Friess
Journal:  Front Hum Neurosci       Date:  2015-11-19       Impact factor: 3.169

  3 in total

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