Literature DB >> 14737922

Linear and nonlinear approaches to the analysis of R-R interval variability.

Autumn Schumacher1.   

Abstract

Analysis techniques derived from linear and non-linear dynamics systems theory qualify and quantify physiological signal variability. Both clinicians and researchers use physiological signals in their scopes of practice. The clinician monitors patients with signal-analysis technology, and the researcher analyzes physiological data with signal-analysis techniques. Understanding the theoretical basis for analyzing physiological signals within one's scope of practice ensures proper interpretation of the relationship between physiolgical function and signal variability. This article explains the concepts of linear and nonlinear signal analysis and illustrates these concepts with descriptions of power spectrum analysis and recurrence quantification analysis. This article also briefly describes the relevance of these 2 techniques to R-to-R wave interval (i.e., heart rate variability) signal analysis and demonstrates their application to R-to-R wave interval data obtained from an isolated rat heart model.

Entities:  

Mesh:

Year:  2004        PMID: 14737922     DOI: 10.1177/1099800403260619

Source DB:  PubMed          Journal:  Biol Res Nurs        ISSN: 1099-8004            Impact factor:   2.522


  14 in total

Review 1.  Heart rate variability: a review.

Authors:  U Rajendra Acharya; K Paul Joseph; N Kannathal; Choo Min Lim; Jasjit S Suri
Journal:  Med Biol Eng Comput       Date:  2006-11-17       Impact factor: 2.602

2.  Signaling prodromes of sudden cardiac death.

Authors:  Ivana Vranic
Journal:  Bosn J Basic Med Sci       Date:  2013-02       Impact factor: 3.363

3.  Autonomic nervous system function in infants with transposition of the great arteries.

Authors:  Tondi M Harrison; Roger L Brown
Journal:  Biol Res Nurs       Date:  2011-05-25       Impact factor: 2.522

4.  Sensitivity to mental effort and test-retest reliability of heart rate variability measures in healthy seniors.

Authors:  Shalini Mukherjee; Rajeev Yadav; Iris Yung; Daniel P Zajdel; Barry S Oken
Journal:  Clin Neurophysiol       Date:  2011-04-02       Impact factor: 3.708

5.  Cardiovascular endurance and heart rate variability in adolescents with type 1 or type 2 diabetes.

Authors:  Melissa Spezia Faulkner; Laurie Quinn; James H Rimmer; Barry H Rich
Journal:  Biol Res Nurs       Date:  2005-07       Impact factor: 2.522

6.  Personalized exercise for adolescents with diabetes or obesity.

Authors:  Melissa Spezia Faulkner; Sara Fleet Michaliszyn; Joseph T Hepworth; Mark D Wheeler
Journal:  Biol Res Nurs       Date:  2013-08-20       Impact factor: 2.522

7.  Computation of nonlinear parameters of heart rhythm using short time ECG segments.

Authors:  Berik Koichubekov; Ilya Korshukov; Nazgul Omarbekova; Viktor Riklefs; Marina Sorokina; Xenia Mkhitaryan
Journal:  Comput Math Methods Med       Date:  2015-01-22       Impact factor: 2.238

8.  Decreases in heart rate variability are associated with postoperative complications in hip fracture patients.

Authors:  Gernot Ernst; Leiv Otto Watne; Frede Frihagen; Torgeir Bruun Wyller; Andreas Dominik; Morten Rostrup
Journal:  PLoS One       Date:  2017-07-25       Impact factor: 3.240

9.  Improving the understanding of sleep apnea characterization using Recurrence Quantification Analysis by defining overall acceptable values for the dimensionality of the system, the delay, and the distance threshold.

Authors:  Sofía Martín-González; Juan L Navarro-Mesa; Gabriel Juliá-Serdá; G Marcelo Ramírez-Ávila; Antonio G Ravelo-García
Journal:  PLoS One       Date:  2018-04-05       Impact factor: 3.240

Review 10.  A Review on the Nonlinear Dynamical System Analysis of Electrocardiogram Signal.

Authors:  Suraj K Nayak; Arindam Bit; Anilesh Dey; Biswajit Mohapatra; Kunal Pal
Journal:  J Healthc Eng       Date:  2018-05-02       Impact factor: 2.682

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