 Technical advance
 Open Access
 Published:
Multiscale Poincaré plots for visualizing the structure of heartbeat time series
BMC Medical Informatics and Decision Making volume 16, Article number: 17 (2015)
Abstract
Background
Poincaré delay maps are widely used in the analysis of cardiac interbeat interval (RR) dynamics. To facilitate visualization of the structure of these time series, we introduce multiscale Poincaré (MSP) plots.
Methods
Starting with the original RR time series, the method employs a coarsegraining procedure to create a family of time series, each of which represents the system’s dynamics in a different time scale. Next, the Poincaré plots are constructed for the original and the coarsegrained time series. Finally, as an optional adjunct, color can be added to each point to represent its normalized frequency.
Results
We illustrate the MSP method on simulated Gaussian white and 1/f noise time series. The MSP plots of 1/f noise time series reveal relative conservation of the phase space area over multiple time scales, while those of white noise show a marked reduction in area. We also show how MSP plots can be used to illustrate the loss of complexity when heartbeat time series from healthy subjects are compared with those from patients with chronic (congestive) heart failure syndrome or with atrial fibrillation.
Conclusions
This generalized multiscale approach to Poincaré plots may be useful in visualizing other types of time series.
Background
The use of delay (also called return) maps is central to the qualitative and quantitative analysis of dynamical systems [1, 2]. The phase space realization with dimension of two and delay of one is referred to as a Poincaré plot [1–3]. This graphical method is widely used to visualize and quantify short and longerterm properties of heart rate variability (HRV) [3–11].
Here we propose a multiscale generalization of the Poincaré plot method, prompted by the observation that physiologic systems generate fluctuations over a broad range of scales. These fluctuations are a marker of the complexity of biologic dynamics, especially in healthy organisms under “freerunning” conditions [12–15]. A variety of computational tools, including fractal and multifractal methods [16–18], multiscale entropy [19–22], and multiscale time irreversibility [23, 24] have been proposed to probe the temporal richness of physiologic signals and of their dynamical alterations with senescence and pathology.
We sought to develop a complementary graphical method to aid in visualizing the multiscale properties of cardiac interbeat interval and other types of time series, in conjunction with these computational analyses. We were further motivated by the pedagogic need for graphical techniques to assist students and trainees in developing an intuitive sense for concepts and terms such as multiscale, selfsimilarity (fractality) and complexity loss. To this end, we adapted and extended the methodology of delay (Poincaré) maps. Classical Poincaré maps are singlescale, since they graph the value of one data point of the original time series against the next. The novelty of our method consists in the generation of multiscale Poincaré (MSP) plots. This multiscale implementation is accomplished via a simple coarsegraining procedure [19, 21] that produces multiple rescaled “copies” of the original signal. For each coarsegrained time series, we create a Poincaré plot, which is then assembled into the final montage. Furthermore, as a potentially useful, but optional adjunct, the data points in each plot are colorcoded using an estimated normalized probability density function to further enhance visualization of time series properties.
To introduce and illustrate the MSP method, we first apply it to synthetic Gaussian white and 1/ftype noise time series. The technique is then applied to RR interval time series obtained in health, chronic (congestive) heart failure and atrial fibrillation. The primary goal here is to introduce this method as a simpletoimplement visualization tool.
Methods
The MSP technique consists of three steps: i) construction of the coarsegrained time series; ii) construction of a Poincaré plot for the original and each of the coarsegrained time series, and iii) colorization of the Poincaré plots based on an estimated normalized probability density function.
Coarsegraining technique and construction of MSP montage
Considering a time series X of length N, X = {x _{1}, x _{2}, x _{3}, …, x _{ N − 1}, x _{ N }}, its Poincaré plot is the scatter plot representing the set of points: (x _{1}, x _{2}), (x _{2}, x _{3}), …, (x _{ N − 1}, x _{ N }) [4–6, 8].
The coarsegrained time series [19, 21] are obtained using a nonoverlapping moving average lowpass filter. The window length, s, determines the scale of the coarsegrained time series {Σ_{s}(j)}. The elements of the coarsegrained time series for scale s are determined according to the equation:
Here, the Poincaré plots for the original and the coarsegrained time series are constructed and assembled into the MSP montage.
Colorization of MSP plots
The traditional monochromatic Poincaré plot can be enhanced by adding color to each of its data points to convey information about their normalized frequency of occurrence [25–27]. The probability density function can be estimated by employing the histogram technique (used here), or employing kernel densitybased or other methods [27–29]. Specifically, we used the Matlab®, dscatter function to compute the smoothed normalized twodimensional histogram of {(x_{i}, x_{i+1})} [30]. We employed the Matlab® “jet” colormap (Fig. 1). We note that alternative color schemes [31, 32] and functions can be used for the same purpose.
Results and discussion
For illustrative purposes, we applied the MSP method to synthetic white and 1/f noise time series and to RR intervals time series in health and selected pathologic states.
MSP plots for synthetic white and 1/f noise time series
Figure 2 shows the MSP montage for a Gaussian white noise time series comprising 20,000 data points. The traditional Poincaré plot (equivalent to scale 1) has a circular shape, due to the normal distribution of the uncorrelated data points (where a uniformly distributed random time series would be represented by a square shape). As expected, the Poincaré plots for scales >1 show the same mean (centroid) with a progressive decrease in circular area. This decrease is due to the relation between the radius of these plots and the standard deviation of each time series. The coarsegraining procedure, by averaging consecutive uncorrelated random points, creates time series with consecutively lower variance. Specifically, the variance of each coarsegrained time series decreases with the scale as σ_{s} ^{2} = σ^{2} /s, where s is the scale and σ^{2} and σ_{s} ^{2} represent the variance of the original and coarsegrained time series, respectively.
Figure 3 shows the MSP montage for a 1/f time series, which represents a complex, fractal structure characterized by correlations between data points across multiple time scales. The conventional (singlescale) Poincaré plot of a 1/f noise time series has an elliptical shape indicating positive correlations between consecutive data points (large values more likely to be followed by large values and low values more likely to be followed by low values). The standard deviation of 1/f noise coarsegrained time series remains constant across scales [21]. Thus, the area of the Poincaré plots remains approximately constant across scales, a consequence of the fractal structure of the 1/f noise signal. (The slight decrease in the area is attributable to the filtering of very high frequency components in a finite time series).
MSP plots for RR interval time series
The MSP technique was then applied to recordings from an openaccess dataset of deidentified cardiac interbeat interval time series from Holter monitor (~24 h) recordings (http://www.physionet.org/challenge/chaos/) [33]. This database includes RR interval time series from ostensibly healthy subjects, as well as patients with congestive (chronic) heart failure (CHF) syndrome, and patients with permanent atrial fibrillation (AF). Here we describe the geometry of the MSP plots from one subject in each of these three groups, representing the extremes of health and heart disease. The MSP plots for the other subjects in each group showed similar characteristics.
Healthy heartbeat dynamics
Figure 4 presents the RR interval time series of a healthy subject, their coarsegrained time series for scales 5 and 10 and corresponding colorized Poincaré plots. The area of these plots is maintained across scales, reminiscent of what is seen with simulated 1/f time series (Fig. 3).
The geometry of the traditional Poincaré plot of heartbeat intervals in health and disease has been the subject of extensive study [4, 6, 11, 34, 35]. The traditional (scale 1) Poincaré plot for healthy subjects exhibits a “cometlike” shape [4]. We confirm this tapered (teardrop) morphology [36, 37], and also find that both the overall shape of the map and its area are preserved across scales. Furthermore, the teardrop appearance for scale 1 is consistent with the previously reported correlation of the average value of the RR interval with the variance of the time series, i.e., shorter RR intervals are associated with lower dispersion (variance) of the RR intervals [4, 38]. The MSP representation (Fig. 5  top panels) highlights information by showing that this asymmetric “tail,” present at scale 1, is preserved across scales for the healthy subject.
Chronic Heart Failure (CHF) and Atrial Fibrillation (AF) Dynamics
Previous reports [3–5] have shown that the area of the Poincaré plot of RR interval time series is markedly smaller for patients with severe CHF (but still in sinus rhythm) than healthy subjects. Here, we extend this finding by showing that the area is invariant under the coarsegraining operation. Our results (Fig. 5  middle panels) are consistent with an overall reduction in multiscale complex variability with heart failure [17, 21].
Prior studies [25, 39] have shown that the Poincaré plots (scale 1) of RR interval time series from subjects with AF are reminiscent of those derived from white noise signals. MSP plots (Fig. 5  bottom panels) highlight these results. In addition, they show that the resemblance between the Poincaré plots of the AF subject and those of white noise signals is most apparent for relatively short time scales (in this example, scales < 10, approximately lower than 15 s). In both white noise and AF cases, the areas of the Poincaré plots decrease with scale. Such behavior is attributable to the uncorrelated structure of the time series fluctuations. However, for larger time scales, the Poincaré plots for the subject with AF show the classical elliptical shape indicative of longrange correlations [40]. This finding is consistent with previous studies [40, 41] reporting that the absolute value of the scaling exponents derived from loglog power spectral plots of RR intervals time series from subjects with AF are closer to 1 (fractal noise) than to 0.5 (white noise) or 2 (brown noise) across the lower frequency bands, with a “crossover” toward those of white noise at higher frequencies.
The persistence of correlated behavior at higher scales (lower frequencies) in AF may be related to the degree to which the atrioventricular (AV) junction and autonomic nervous system function are preserved in this common arrhythmia. Whether AF associated with the most severe derangements of AV nodal conduction (and concomitant myocardial disease) shows a complete breakdown of correlations is an intriguing question with basic and clinical implications. The MSP method may be of use in screening “big datasets” in order to gain some intuition about the multiscale behavior of RR intervals in AF in different clinical subsets. We hypothesize that permanent AF associated with heart failure would have a less complex structure by this method than socalled “lone” AF, which is not associated with clinically apparent heart disease.
In this regard, Fig. 6 shows an example of AF [25] from another patient. The multiscale Poincaré plots reveal a different pattern of variability on both shorter and longer time scales compared with that shown in the bottom panel of Fig. 5. First is a short time scale clustering of RR intervals, embedded in the overall map. These additional clusters correspond to alternation of RR intervals which has been noted before in some cases of AF [25, 39, 42], but remains to be mechanistically explained and clinically investigated further. One possible explanation is dualpathway AV conduction [25, 39]; another is a Wenckebach variant of conduction block in the AV node. The finding was not due to ventricular ectopy here. Second, the MSP plots add information by revealing that this anomalous pattern is scalespecific, as it is not apparent with coarse graining. In contrast, MSP analysis of RR time series from healthy subjects shows that the “tail” at the lower left portion of the plots (due to decreased variance with increased heart rate) is present across scales (Fig. 4). While “anecdotal,” these examples support the possible utility of using MSP plots in evaluating subsets of RR time series in health and disease having quantitatively and qualitatively different interbeat interval dynamics that may not be readily discernible using conventional time series inspection and analysis. More generally, the above findings are consistent with the concept that perturbations related to advanced aging and pathology (e.g., heart failure, atrial fibrillation, etc.) may be most evident in disturbances in higher frequency fluctuations, those required for “finetuning” adaptiveness [13, 14, 43].
Use of colorization
Finally, we note that the colorization of the MSP plots, an optional feature of the multiscale renderings, is intended to facilitate rapid assessment of the values of the most frequently observed pairs of RR intervals (mode) as well as of the shape of the probability density function. For example, Fig. 5 shows that the most frequently observed values are ~1 s for the healthy subject, ~0.75 s for the patient with CHF and ~ 0.5 s for the patient with AF. In addition, Fig. 5 also shows that the probability density function is skewed to the left in the case of the healthy subject and to the right in case of the CHF patient and the subject with AF on short time scales. Whether quantitative analyses developed for traditional Poincaré plots [5, 6, 8, 11, 35, 44] can be usefully extended to MSP plots is of interest but outside the scope of this brief methodological note. We also emphasize that these plots are intended to complement current quantitative methods of time series analysis (e.g., Fourier, fractal/multifractal, and entropyrelated analyses, to name but a few).
Conclusions
We introduce a novel delay map implementation termed multiscale Poincaré (MSP) plots, to facilitate visualization of multiscale structure of cardiac interbeat interval time series. The method comprises: i) a coarsegraining operation that generates a family of time series; ii) delay map construction for the original and the coarsegrained time series; and iii) colorization of the delay maps based on an estimated normalized probability density function. The method appears to be useful in depicting concepts such as scaling behavior in health and disease and contrasting “realworld” and simulated data. Future studies are needed to evaluate its use in heart rate dynamics, as well as its potential utility in studying other types of time series.
Abbreviations
 AF:

Atrial fibrillation
 AV:

Atrioventricular
 CHF:

Congestive (chronic) heart failure
 HRV:

Heart rate variability
 MSP:

Multiscale Poincaré
References
 1.
Ruelle D, Takens F. On the nature of turbulence. Commun Math Phys. 1971;20:167–92.
 2.
Takens F. Detecting strange attractors in turbulence. Lect Notes Math. 1981;898:366–81.
 3.
Lerma C, Infante O, PérezGrovas H, José MV. Poincaré plot indexes of heart rate variability capture dynamic adaptations after haemodialysis in chronic renal failure patients. Clin Physiol Funct Imaging. 2003;23(2):72–80.
 4.
Woo MA, Stevenson WG, Moser DK, Trelease RB, Harper RM. Patterns of beattobeat heart rate variability in advanced heart failure. Am Heart J. 1992;123(3):704–10.
 5.
Kamen PW, Tonkin AM. Application of the Poincaré plot to heart rate variability: a new measure of functional status in heart failure. Aust N Z J Med. 1995;25(1):18–26.
 6.
Kamen PW, Krum H, Tonkin AM. Poincaré plot of heart rate variability allows quantitative display of parasympathetic nervous activity in humans. Clin Sci (Lond). 1996;91(2):201–8.
 7.
Tulppo MP, Mäkikallio TH, Takala TE, Seppänen T, Huikuri HV. Quantitative beattobeat analysis of heart rate dynamics during exercise. Am J Physiol Heart Circ Physiol. 1996;271(1):H244–52.
 8.
Brennan M, Palaniswami M, Kamen P. Do existing measures of Poincaré plot geometry reflect nonlinear features of heart rate variability? IEEE Trans Biomed Eng. 2001;48(11):1342–7.
 9.
Stein PK, Reddy A. Nonlinear heart rate variability and risk stratification in cardiovascular disease. Indian Pacing Electrophysiol J. 2005;5(3):210–20.
 10.
Stein PK, Domitrovich PP, Hui N, Rautaharju P, Gottdiener J. Sometimes higher heart rate variability is not better heart rate variability: results of graphical and nonlinear analyses. J Cardiovasc Electrophysiol. 2005;16(9):954–9.
 11.
Khandoker AH, Karmakar C, Brennan M, Palaniswami M, Voss A. Poincaré plot methods for heart rate variability analysis. New York: Springer; 2013.
 12.
Goldberger AL, West BJ. Fractals in physiology and medicine. Yale J Biol Med. 1987;60(5):421–35.
 13.
Goldberger AL, Amaral LA, Hausdorff JM, Ivanov PC, Peng CK, Stanley HE. Fractal dynamics in physiology: alterations with disease and aging. Proc Natl Acad Sci USA. 2002;99 suppl 1:2466–72.
 14.
Goldberger AL, Giles F. Filley lecture: Complex systems. Proc Am Thorac Soc. 2006;3(6):467–71.
 15.
Voss A, Schulz S, Schroeder R, Baumert M, Caminal P. Methods derived from nonlinear dynamics for analysing heart rate variability. Philos Trans A Math Phys Eng Sci. 2009;367(1887):277–96.
 16.
Peng CK, Havlin S, Stanley HE, Goldberger AL. Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos. 1995;5(1):82–7.
 17.
Ivanov PC, Amaral LA, Goldberger AL, Havlin S, Rosenblum MG, Struzik ZR, et al. Multifractality in human heartbeat dynamics. Nature. 1999;399(6735):461–5.
 18.
Kantelhardt JW, Zschiegner SA, KoscielnyBunde E, Havlin S, Bunde A, Stanley HE. Multifractal detrended fluctuation analysis of nonstationary time series. Physica A. 2002;316(1):87–114.
 19.
Costa MD, Goldberger AL, Peng CK. Multiscale entropy analysis of complex physiologic time series. Phys Rev Lett. 2002;89(6):068102.
 20.
Costa MD, Peng CK, Goldberger AL, Hausdorff JM. Multiscale entropy analysis of human gait dynamics. Physica A. 2003;330(1):53–60.
 21.
Costa MD, Goldberger AL, Peng CK. Multiscale entropy analysis of biological signals. Phys Rev E Stat Nonlin Soft Matter Phys. 2005;71(2):021906.
 22.
Wu SD, Wu CW, Lin SG, Lee KY, Peng CK. Analysis of complex time series using refined composite multiscale entropy. Phys Lett A. 2014;378(20):1369–74.
 23.
Costa MD, Goldberger AL, Peng CK. Broken asymmetry of the human heartbeat: loss of time irreversibility in aging and disease. Phys Rev Lett. 2005;95(19):198102.
 24.
Costa MD, Peng CK, Goldberger AL. Multiscale analysis of heart rate dynamics: entropy and time irreversibility measures. Cardiovasc Eng. 2008;8(2):88–93.
 25.
Burykin A, Costa MD, Citi L, Goldberger AL. Dynamical density delay maps: simple, new method for visualising the behaviour of complex systems. BMC Med Inform Decis Mak. 2014;14(1):6.
 26.
Henriques T, Munshi MN, Segal AR, Costa MD, Goldberger AL. “GlucoseataGlance”: new method to visualize the dynamics of continuous glucose monitoring data. J Diabetes Sci Technol. 2014;8(2):299–306.
 27.
Burykin A, Mariani S, Henriques T, Silva TF, Schnettler WT, Costa MD, et al. Remembrance of time series past: simple chromatic method for visualizing trends in biomedical signals. Physiol Meas. 2015;36(7):N95–102.
 28.
Rosenblatt M. Remarks on some nonparametric estimates of a density function. Ann Math Stat. 1956;27:832–7.
 29.
Lake DE. Nonparametric entropy estimation using kernel densities. Meth Enzymol. 2009;467:531–46.
 30.
Eilers PH, Goeman JJ. Enhancing scatterplots with smoothed densities. Bioinformatics. 2004;20(5):623–8.
 31.
Ware C. Color sequences for univariate maps: Theory, experiments and principles. IEEE Comput Graph Appl. 1988;8(5):41–9.
 32.
Gehlenborg N, Wong B. Points of view: Mapping quantitative data to color. Nat Methods. 2012;9(8):769.
 33.
Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PC, Mark RG, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation. 2000;101(23):e215–20.
 34.
Malik M, Bigger JT, Camm AJ, Kleiger RE, Malliani A, Moss AJ, et al. Heart rate variability standards of measurement, physiological interpretation, and clinical use. Eur Heart J. 1996;17(3):354–81.
 35.
Piskorski J, Guzik P. Geometry of Poincaré plot of RR intervals and its asymmetry in healthy adults. Physiol Meas. 2007;28(3):287–300.
 36.
Von Seggern DH. CRC Standard Curves and Surfaces with Mathematica. Boca Raton: CRC; 2006.
 37.
Weisstein EW. Teardrop curve. From MathWorldA Wolfram Web Resource. http://mathworld.wolfram.com/TeardropCurve.html. Accessed 30 Sep 2015.
 38.
Monfredi O, Lyashkov AE, Johnsen AB, Inada S, Schneider H, Wang R, et al. Biophysical characterization of the underappreciated and important relationship between heart rate variability and heart rate. Hypertension. 2014;64(6):1334–43.
 39.
Climent AM, de la Salud GM, Husser D, Castells F, Millet J, Bollmann A. Poincaré surface profiles of RR intervals: a novel noninvasive method for the evaluation of preferential AV nodal conduction during atrial fibrillation. IEEE Trans Biomed Eng. 2009;56(2):433–42.
 40.
Hayano J, Yamasaki F, Sakata S, Okada A, Mukai S, Fujinami T. Spectral characteristics of ventricular response to atrial fibrillation. Am J Physiol Heart Circ Physiol. 1997;273(6):H2811–6.
 41.
Balocchi R, Carpeggiani C, Fronzoni L, Peng CK, Michelassi C, Mietus J, et al. Short and longterm heart rate dynamics in atrial fibrillation. Stud Health Technol Inform. 1999;60:91–6.
 42.
Masè M, Marini M, Disertori M, Ravelli F. Dynamics of AV coupling during human atrial fibrillation: role of atrial rate. Am J Physiol Heart Circ Physiol. 2015;301(1):H198–205.
 43.
Goldberger AL, Findley L, Blackburn MJ, Mandell AJ. Nonlinear dynamics of heart failure: implications of longwavelength cardiopulmonary oscillations. Am Heart J. 1984;107(3):612–5.
 44.
Guzik P, Piskorski J, Krauze T, Wykretowicz A, Wysocki H. Heart rate asymmetry by Poincaré plots of RR intervals. Biomed Tech (Berl). 2006;51(4):272–5.
Acknowledgments
This work was supported by the Wyss Institute, the G. Harold & Leila Y. Mathers Foundation, the National Institutes of Health grants R24HL114473 and 2R01GM104987. We gratefully acknowledge the advice and input of Dr. Madalena D. Costa.
Author information
Affiliations
Corresponding author
Additional information
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
The conceptualization and initial implementation of the method were developed by TFS, FR, AB, and ALG. TSH, SM, and ALG contributed with refinements of the method, analysis of the findings and to the composition and editing of the manuscript. All authors have read and approved this manuscript.
Rights and permissions
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
About this article
Cite this article
Henriques, T.S., Mariani, S., Burykin, A. et al. Multiscale Poincaré plots for visualizing the structure of heartbeat time series. BMC Med Inform Decis Mak 16, 17 (2015). https://doi.org/10.1186/s1291101602520
Received:
Accepted:
Published:
Keywords
 Atrial fibrillation
 Complexity
 Congestive heart failure
 Fractal
 Heart rate
 Multiscale
 Nonlinear dynamics
 Poincaré plot
 Time series
 Visualization