Literature DB >> 1487941

Improved procedure of complex demodulation and an application to frequency analysis of sleep spindles in EEG.

Y L Hao1, Y Ueda, N Ishii.   

Abstract

Complex demodulation is a local version of harmonic analysis that enables the amplitude and phase of particular frequency components of a time series to be described as functions of time. The paper presents a computational procedure involving complex demodulation with interpolation of data in the frequency domain. A computational procedure comprising repeated use of complex demodulation is also presented. This is used to estimate the optimum choice of the demodulating frequency which considerably influences the measurement of the instantaneous amplitude and phase of the underlying process. The usefulness of this procedure is verified by computer simulation. An example of applying this procedure to the estimation of the centre and the instantaneous frequencies of sleep spindles in the EEG (electroencephalogram) is presented. By using the procedure developed here, several partially overlapping sleep spindles are detected and correctly separated. The paper also presents an approach to separating and analysing transient time series (such as overlapping sleep spindles) by using an accurate frequency processing technique.

Mesh:

Year:  1992        PMID: 1487941     DOI: 10.1007/bf02446168

Source DB:  PubMed          Journal:  Med Biol Eng Comput        ISSN: 0140-0118            Impact factor:   2.602


  5 in total

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Authors:  W R Jankel; E Niedermeyer
Journal:  J Clin Neurophysiol       Date:  1985-01       Impact factor: 2.177

  5 in total
  8 in total

1.  Analysis of A-phase transitions during the cyclic alternating pattern under normal sleep.

Authors:  Martin Oswaldo Mendez; Ioanna Chouvarda; Alfonso Alba; Anna Maria Bianchi; Andrea Grassi; Edgar Arce-Santana; Guilia Milioli; Mario Giovanni Terzano; Liborio Parrino
Journal:  Med Biol Eng Comput       Date:  2015-08-08       Impact factor: 2.602

2.  Artificial neural network and wavelet based automated detection of sleep spindles, REM sleep and wake states.

Authors:  Rakesh Kumar Sinha
Journal:  J Med Syst       Date:  2008-08       Impact factor: 4.460

3.  Enhanced automated sleep spindle detection algorithm based on synchrosqueezing.

Authors:  Muammar M Kabir; Reza Tafreshi; Diane B Boivin; Naim Haddad
Journal:  Med Biol Eng Comput       Date:  2015-03-17       Impact factor: 2.602

4.  The demodulated band transform.

Authors:  Christopher K Kovach; Phillip E Gander
Journal:  J Neurosci Methods       Date:  2015-12-19       Impact factor: 2.390

5.  Beta modulation reflects name retrieval in the human anterior temporal lobe: an intracranial recording study.

Authors:  Taylor J Abel; Ariane E Rhone; Kirill V Nourski; Timothy K Ando; Hiroyuki Oya; Christopher K Kovach; Hiroto Kawasaki; Matthew A Howard; Daniel Tranel
Journal:  J Neurophysiol       Date:  2016-04-13       Impact factor: 2.714

6.  A time-frequency analysis of the dynamics of cortical networks of sleep spindles from MEG-EEG recordings.

Authors:  Younes Zerouali; Jean-Marc Lina; Zoran Sekerovic; Jonathan Godbout; Jonathan Dube; Pierre Jolicoeur; Julie Carrier
Journal:  Front Neurosci       Date:  2014-10-28       Impact factor: 4.677

7.  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

8.  Dynamic coupling between slow waves and sleep spindles during slow wave sleep in humans is modulated by functional pre-sleep activation.

Authors:  Juliana Yordanova; Roumen Kirov; Rolf Verleger; Vasil Kolev
Journal:  Sci Rep       Date:  2017-11-03       Impact factor: 4.379

  8 in total

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