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breathing patterns. In: Rhythms in Physiological Systems, H.
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171-191.
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derived from quantification of recurrence plots. Phys. Lett .
A 171: 199-203.
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physiological systems and states using recurrence plot strategies.
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isometric loading on biceps EMG dynamics as assessed by linear and
nonlinear tools. J. Appl. Physiol. 78: 814-822.
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structures in physiological systems using recurrence plot strategies.
In: Bioengineering Approaches to Pulmonary Physiology and
Medicine. M.C.K. Khoo (ed.) Plenum Press, New York, Chapter 8, pp
137-148.
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Recurrence quantification analysis of the logistic equation with
transients. Phys. Lett. A 223: 255-26.
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quantification analysis and principle components in the detection of short
complex signals. Phys. Lett. A 237: 131-135.
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variability using methods derived from nonlinear dynamics. In:
Analysis and Assessment of Cardiovascular Function. G. Drzewiecki
and J.K.-J. Li (eds.). Springer Verlag, New York, Chapter 19, pp.
324-334.
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Recurrence quantification analysis in structure-function relationships
of proteins: an overview of a general methodology applied to the case of
TEM-1 beta-lactamase. Protein Engin. 11: 87-93.
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deterministic signals in exceptionally noisy environments using
cross-recurrence quantification. Physics Lett. A 246:
122-128.
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dynamical and spatial systems. In: Complexity in the Living: A
Modelistic Approach. A. Colosimo (ed.). Proc. Int. Meet., Feb. 1997,
University of Rome "La Sapienza," pp. 101-133.
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J.P. (1999). Recurrence quantification analysis as a tool for the
characterization of molecular dynamics sumilations. Physical Rev.
E. 59: 992-998.
- Manetti, C., Ceruso, M.-A., Giuliani, A., Webber, C.L., Jr., Zbilut,
J.P. (1999). Recurrence quantification analysis in molecular
dynamics. Annl. N.Y. Acad. Sci. 879: 258-266.
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(1999). Orthographic structuring of human speech and texts: linguistic
application of recurrence quantification analysis. Int. J. Chaos
Theory Appl. 4: 21-28.
- Zbilut, J.P., Colosimo, A., Webber, C.L., Jr., Giuliani A. (2000).
The role of hydrophobicity patterns in prion folding as revealed by
recurrence quantification analysis of primary structures. Protein
Eng. 13: 99-104.
- Ikegawa, S., Shinohara, M., Fukunaga, T., Zbilut, J.P., Webber, C.L.,
Jr. (2000). Nonlinear time-course of lumbar muscle fatigue using
recurrence quantifications. Biol. Cybernetics 82: 373-382.
- Zbilut, J.P., Hu, Z., Webber, C.L., Jr. (2000). Singularities of
the heart beat as demonstrated by recurrence quantification analysis.
Proc. Eng. Med. Biol. Soc. CD ROM.
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Recurrence quantification analysis as an empirical test to distinguish
deterministic versus random number series. Phys. Lett. A 267:
174-178.
- Thomasson, N., Hoeppner, T.J., Webber, C.L., Jr., and Zbilut, J.P.
(2001). Recurrence quantification in epileptic EEG's. Phys.
Lett. A 279: 94-101.
- Manetti, C., Giuliani, A., Ceruso, M.-A., Cannistraro, S., Webber,
C.L., Jr., Zbilut, J.P. (2001). Recurrence analysis of hydration
effects on nonlinear protein dynamics: multiplicative scaling and additive
processes. Phys. Lett. A 281: 317-323.
- Webber, C.L., Jr., Giuliani, A., Zbilut, J.P., Colosimo, A. (2001).
Elucidating protein secondary structures using alpha-carbon recurrence
quantifications. Protein: Struct. Funct. Gen. 44: 292-303.
- Giuliani, A., Colafranceschi, M., Webber, C.L., Jr., Zbilut, J.P.
(2001). A complexity score derived from principle components analysis
of nonlinear order measures. Physica A 301:567-588.
- Zbilut, J.P., Sirabella, P., Giuliani, A., Manetti, C., Colosimo, A.,
Webber, C.L., Jr. (2002). Review of nonlinear analysis of proteins
through recurrence quantification. Cell. Biochem. Biophys. 36:
67-87.
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Recurrence quantification analysis as a tool for nonlinear exploration
of nonstationary cardiac signals. Med. Engin. Physics 24:
53-60.
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P., Colosimo, A. (2002). Nonlinear signal analysis methods in the
elucidation of protein sequence/structure relationships. Chem.
Rev. 102: 1471-1491.
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Application of recurrence quantification analysis to EEG signals.
Int. J. Comp. Appl. 9: 1-6.
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Cross recurrence quantification of coupled oscillators. Phys.
Lett. 305: 59-69.
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C., Valerio, M.-C., Webber, C.L., Jr., Giuliani, A. (2003). Protein
aggratation/folding: the role of deterministic singularities of sequence
hydrophobicity as determined by nonlinear signal analysis of
acylphosphatase and A-beta(1-40). Biophysical J. 85:
3544-3557.
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recurrence quantifications in dynamic exercise. Biol. Cybernetics
90: 337-348.
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paterns and net charge. In: Complexity in the living: A problem-oriented approach. A. Colosimo, ed., Proc. Int. Meet., Sept. 2004, University of Rome "La Sapienza," pp. 139-157.
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the folding mechanism and aggregation of proteins: a computational approach. J. Proteome Res. 3:1243-1253.
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hydrophobicity. Phys. Lett. A 346:33-41.
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sciences, (Chapter 2, pp. 26-94), M.A. Riley, G. Van Orden, eds. Retrieved December 1, 2004 http://www.nsf.gov/sbe/bcs/pac/nmbs/nmbs.pdf
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DOI: 10.1002/9780471740360.edb1355
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semiotics. Physica A 361: 665-676.
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Piano, M.R. (2006). Detection of cardiac variability in the isolated
rat heart. Physiol Meth. Nrusing Res. 8: 55-66.
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http://www.neurojournal.com/article/view/496/553
- Zbilut, J.P., Chua, G.H., Krishnan, A., Bossa, C., Rother, K., Webber, C.L., Jr., Giuliani, A. (2007). A topologically related singularity suggests a maximum preferred size in
protein domains. Proteins: Struct., Funct., Bioinformatics 66:621-629.
RECURRENCE PLOTS AND QUANTIFICATION OF RECURRENCES are
illustrated below for data derived from a normal human electrocardiogram
(continuous flow). The input data for each plot consist of the same 480
consecutive RR intervals (discrete map) embedded in 5 dimensions. A local
recurrence plot (left) is generated by keeping the radius low (15% of
maxdist), allowing recurrences to occur only in near neighborhoods. A
global recurrence plot (right) is generated by saturating the radius (100%
of maxdist), allowing for all possible recurrences. The various colors
reflect different Euclidean distances between trajectories and are
analogous to geographical relief maps. Five RQA variables are computed
(%recurrence, %determinism, information entropy, maximum diagonal line
length, state trend) which have relavancy as nonlinear markers of changes
in dynamical systems and physiological states. For specific details
regarding RQA implementation (easy) and RQA interpretaion (difficult), see
our publications (above) and free software release (below).
LOCAL RECURRENCE PLOT
|
GLOBAL RECURRENCE PLOT
|
RQA SOFTWARE that generates images like those above and other RQA utilities are available in a self-extracting file (Download
Software ver 12.1). The current distribution version of this software includes 30 executable RQA programs, 10 utility programs, 21 example data files, and an
explanatory README.PDF file and monograph chapter 2.PDF. Please contact Dr. Webber directly (phone, facsimile, electronic mail, snail mail) with any questions
regarding research applications of these RQA programs.
TEL: (708) 216-3343
FAX: (708) 216-6308
EML: cwebber@lumc.edu
HITS: 
Created: August 22, 1996
Revised: January 7, 2008
Research supported in part by: NSF/NIH 0240230
