Literature DB >> 29110182

New complexity measures reveal that topographic loops of human alpha phase potentials are more complex in drowsy than in wake.

Aleksandar Kalauzi1, Aleksandra Vuckovic2, Tijana Bojić3.   

Abstract

A number of measures, stemming from nonlinear dynamics, exist to estimate complexity of biomedical objects. In most cases they are appropriate, but sometimes unconventional measures, more suited for specific objects, are needed to perform the task. In our present work, we propose three new complexity measures to quantify complexity of topographic closed loops of alpha carrier frequency phase potentials (CFPP) of healthy humans in wake and drowsy states. EEG of ten adult individuals was recorded in both states, using a 14-channel montage. For each subject and each state, a topographic loop (circular directed graph) was constructed according to CFPP values. Circular complexity measure was obtained by summing angles which directed graph edges (arrows) form with the topographic center. Longitudinal complexity was defined as the sum of all arrow lengths, while intersecting complexity was introduced by counting the number of intersections of graph edges. Wilcoxon's signed-ranks test was used on the sets of these three measures, as well as on fractal dimension values of some loop properties, to test differences between loops obtained in wake vs. drowsy. While fractal dimension values were not significantly different, longitudinal and intersecting complexities, as well as anticlockwise circularity, were significantly increased in drowsy. Graphical abstract An example of closed topographic carrier frequency phase potential (CFPP) loops, recorded in one of the subjects in the wake (A) and drowsy (C) states. Lengths of loop graph edges, r(c j, c j + 1), plotted against the series of EEG channels with decreasing CFPP values, c j , in the wake (B) and drowsy (D) states. Conventional fractal analysis did not reveal any difference between them; therefore, three new complexity measures were introduced.

Entities:  

Keywords:  Alpha activity; Circular graphs; Complexity; Phase potentials; Wake and drowsy

Mesh:

Year:  2017        PMID: 29110182     DOI: 10.1007/s11517-017-1746-3

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


  32 in total

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Authors:  J G Stinstra; M J Peters
Journal:  Med Biol Eng Comput       Date:  1998-11       Impact factor: 2.602

2.  Comparison of fractal and power spectral EEG features: effects of topography and sleep stages.

Authors:  Béla Weiss; Zsófia Clemens; Róbert Bódizs; Péter Halász
Journal:  Brain Res Bull       Date:  2010-12-13       Impact factor: 4.077

3.  Non-linear analysis of EEG signals at various sleep stages.

Authors:  Rajendra Acharya U; Oliver Faust; N Kannathal; TjiLeng Chua; Swamy Laxminarayan
Journal:  Comput Methods Programs Biomed       Date:  2005-10       Impact factor: 5.428

4.  Where the BOLD signal goes when alpha EEG leaves.

Authors:  H Laufs; John L Holt; Robert Elfont; Michael Krams; Joseph S Paul; K Krakow; A Kleinschmidt
Journal:  Neuroimage       Date:  2006-03-13       Impact factor: 6.556

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Journal:  Electroencephalogr Clin Neurophysiol       Date:  1987-09

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Journal:  Comput Biol Med       Date:  1988       Impact factor: 4.589

Review 7.  A comparative review on sleep stage classification methods in patients and healthy individuals.

Authors:  Reza Boostani; Foroozan Karimzadeh; Mohammad Nami
Journal:  Comput Methods Programs Biomed       Date:  2016-12-10       Impact factor: 5.428

8.  Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources.

Authors:  Cornelis J Stam; Guido Nolte; Andreas Daffertshofer
Journal:  Hum Brain Mapp       Date:  2007-11       Impact factor: 5.038

9.  The sleep slow oscillation as a traveling wave.

Authors:  Marcello Massimini; Reto Huber; Fabio Ferrarelli; Sean Hill; Giulio Tononi
Journal:  J Neurosci       Date:  2004-08-04       Impact factor: 6.167

Review 10.  EEG alpha oscillations: the inhibition-timing hypothesis.

Authors:  Wolfgang Klimesch; Paul Sauseng; Simon Hanslmayr
Journal:  Brain Res Rev       Date:  2006-08-01
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