Assessing HRV
http://herkules.oulu.fi/isbn9514250133/html/
Summary
Clinical and Experimental Hypertension
2005, Vol. 27, No. 2-3, Pages 149-158
Fractal and Complexity Measures of Heart Rate Variability
Juha S Perkiömäki, M.D.1,2, Timo H Mäkikallio, M.D.1 and Heikki V Huikuri, M.D.1
1
Division of Cardiology, Department of Medicine, University of Oulu, Oulu, Finland
2
Division of Cardiology, Department of Medicine, University of Oulu, P.O. Box 5000
(Kajaanintie 50), Oulu, FIN-90014, Finland
Heart rate variability has been analyzed conventionally with time and frequency domain
methods, which measure the overall magnitude of RR interval fluctuations around its mean value
or the magnitude of fluctuations in some predetermined frequencies. Analysis of heart rate
dynamics by methods based on chaos theory and nonlinear system theory has gained recent
interest. This interest is based on observations suggesting that the mechanisms involved in
cardiovascular regulation likely interact with each other in a nonlinear way. Furthermore, recent
observational studies suggest that some indexes describing nonlinear heart rate dynamics, such
as fractal scaling exponents, may provide more powerful prognostic information than the
traditional heart rate variability indexes. In particular, the short-term fractal scaling exponent
measured by the detrended fluctuation analysis method has predicted fatal cardiovascular events
in various populations. Approximate entropy, a nonlinear index of heart rate dynamics, that
describes the complexity of RR interval behavior, has provided information on the vulnerability
to atrial fibrillation. Many other nonlinear indexes, e.g., Lyapunov exponent and correlation
dimensions, also give information on the characteristics of heart rate dynamics, but their clinical
utility is not well established. Although concepts of chaos theory, fractal mathematics, and
complexity measures of heart rate behavior in relation to cardiovascular physiology or various
cardiovascular events are still far away from clinical medicine, they are a fruitful area for future
research to expand our knowledge concerning the behavior of cardiovascular oscillations in
normal healthy conditions as well as in disease states.
http://www.bmhegde.com/wavelet.html
2008 International Conference on BioMedical Engineering and Informatics
Nonlinear Dynamics Techniques for the Detection of the Brain Areas Using MER Signals
May 27-May 30
ISBN: 978-0-7695-3118-2
ASCII Text
x
Andrea Rodr?guez-S?nchez, Edilson Delgado-Trejos, ?lvaro Orozco-Guti?rrez, Germ?
Castellanos-Dom?nguez, Enrique Guijarro-Estell?, "Nonlinear Dynamics Techniques for the
Detection of the Brain Areas Using MER Signals," BioMedical Engineering and Informatics,
International Conference on, vol. 2, pp. 198-202, 2008 International Conference on
BioMedical Engineering and Informatics, 2008.
BibTex
x
@article{ 10.1109/BMEI.2008.330,
author = {Andrea Rodr?guez-S?nchez and Edilson Delgado-Trejos and ?lvaro OrozcoGuti?rrez and Germ? Castellanos-Dom?nguez and Enrique Guijarro-Estell?},
title = {Nonlinear Dynamics Techniques for the Detection of the Brain Areas Using MER
Signals},
journal ={BioMedical Engineering and Informatics, International Conference on},
volume = {2},
year = {2008},
isbn = {978-0-7695-3118-2},
pages = {198-202},
doi = {http://doi.ieeecomputersociety.org/10.1109/BMEI.2008.330},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
Andrea Rodr?guez-S?nchez
Edilson Delgado-Trejos
?lvaro Orozco-Guti?rrez
Germ? Castellanos-Dom?nguez
Enrique Guijarro-Estell?
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/BMEI.2008.330
A methodology for identifying brain areas from the brain MER signals (microelectrode
recordings) is presented, which is based on a nonlinear feature set. We propose nonlinear
dynamics measures such as correlation dimension, Hurst exponent and the largest Lyapunov
exponent to characterize the dynamic structure. The MER records belong to the Polytechnical
University of Valencia, 24 records for each zone (black substance, thalamus, subthalamus
nucleus and uncertain area). The detection of each area using characteristics derived from
complexity analysis was obtained through a classifier (support vector machine). The joint
information between areas is remarkable and the best accuracy result was 93.75%. The nonlinear
dynamics techniques help to discriminate the four brain areas considered, since they take into
account the intrinsic dynamics of the signals and the structures analysis based on the multivariate
statistical procedures is an important step in the data preprocessing.
Vol. 65, No. 2, 2008
Free Abstract
Article (References)
Article (PDF 232 KB)
Original Article
Detrended Fluctuation Analysis of Heart Rate Variability in Normal and
Growth-Restricted Fetuses
Akihiko Kikuchia, Nobuya Unnob, Shiro Kozumac, Yuji Taketanic
a
Department of Obstetrics, Center for Perinatal Medicine, Nagano Children's
Hospital, Nagano,
b
Department of Obstetrics and Gynecology, School of Medicine, Kitasato
University, Kanagawa, and
c
Department of Obstetrics and Gynecology, Faculty of Medicine, University
of Tokyo, Tokyo, Japan
Address of Corresponding Author
Gynecol Obstet Invest 2008;65:116-122 (DOI: 10.1159/000109266)
Key Words
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Fetal heart rate
Heart rate variability
Detrended fluctuation analysis
Intrauterine growth restriction
Small-for-gestational-age fetus
Abstract
Background: Detrended fluctuation analysis (DFA) has recently been
validated as an excellent method by which to analyze heart rate variability
and distinguish healthy subjects from patients with various types of the
cardiac nervous system dysfunction. Methods: One hundred and nineteen
fetal heart rate (FHR) recordings obtained from healthy normal fetuses and 68
recordings obtained from small-for-gestational-age (SGA) fetuses were
analyzed by DFA to examine gestational and pathologic changes of the
scaling exponent, . Results: In normal fetuses, a significant increase was
observed in both the short-term ( 30 s) 1 and long-term (>30 s) 2 scaling
exponents according to gestational age. The 1 values of SGA fetuses were
not significantly different from those of healthy normal fetuses; however, the
2 values of the former group (0.955 ± 0.152) were significantly higher than
those of normal subjects (0.887 ± 0.128; p = 0.001). Conclusion: The 2
exponent appears to be a sensitive probe for detecting subtle, and possibly
important, changes that occur in fetuses with intrauterine growth restriction,
and may be helpful in the early and noninvasive detection of placental
insufficiency or incipient intrauterine growth restriction. The use of DFA
techniques offers great promise for understanding FHR behavior.
Copyright © 2007 S. Karger AG, Basel