Dynamic Decomposition of Poincaré Plots for Multivariate Analysis and Visualization of Simultaneously Recorded Physiological Time Series
Piskorski Jarosław 1*, Guzik Przemysław 2
1Institute of Physics, University of Zielona Góra,
ul. Szafrana 4a, Zielona Góra, Poland
2Department of Cardiology – Intensive Therapy and Internal Diseases, Poznań University of Medical Sciences,
ul. Przybyszewskiego 49, Poznań, Poland
*e-mail: J.Piskorski@proton.if.uz.zgora.pl
Received:
Received: 23 March 2010; accepted 12 July 2010; published online: 13 September 2010
DOI: 10.12921/cmst.2010.16.02.181-186
OAI: oai:lib.psnc.pl:728
Abstract:
We apply the dynamic decomposition of Poincaré plots, which is a computationally intensive, visual method of analysing physiological time series, to the analysis of the interbeat interval variability, systolic blood pressure, stroke volume and total peripheral resistance which were simultaneously recorded from a patient with pheochromocytoma and rapid, repetitive haemodynamic changes over 6 hours. The resulting animation is analysed and interpreted. It is found that changes in total peripheral resistance usually precede those of other variables, and the magnitude of changes is greatest for this variable. It is demonstrated that the decomposition of Poincaré plots of multivariate signals can visualise both the order and the extent of ongoing instant changes.
Key words:
haemodynamics, heart rate variability, medical visualisation, pheochromocytoma, Poincaré plot
References:
[1] A.J.E. Seely, P.T. Macklem, Complex systems and the technology of variability analysis. Critic. Care 6, R367-84 (2004).
[2] M. Brennan, M. Palaniswami, P. Kamen, Do existing measures of Poincaré plot geometry reflect nonlinear features of Heart Rate Variability? IEEE Trans. Biomed. Eng. 48, 1342-1347 (2001).
[3] A. Voss, R. Schroeder, G. Trubner, M. Goering, H.R. Figulla, A. Schirdewan, Comparison of nonlinear methods symbolic dynamics, detrended fluctuation, and Poincaré plot analysis
in risk stratification in patients with dilated cardiomyopathy. Chaos 17, 015120-1-7 (2007).
[4] S. Guzzetti, M.G. Signorini, C. Cogliati, S. Mezzetti, A. Porta, S. Cerutti, A. Malliani, Non-linear dynamics and chaotic indices in heart rate variability of normal subjects and
heart-transplanted patients. Cardiovasc. Res. 31, 441-446 (1995).
[5] M. Costa, A.L. Goldberger, C K. Peng, Multiscale entropy analysis of biological signals. Phys. Rev. E 71, 021906-1- 18 (2005).
[6] P. Guzik, A. Wykretowicz, K.H. Wesseling, H. Wysocki, Adrenal pheochromocytoma associated with dramatic cyclic haemodynamic fluctuations. Int. J. Cardiol. 103, 351-353 (2005).
[7] P. Guzik, J. Piskorski, Decomposition of the commet-like Poincaré plot s of RR intervals. Folia Cardiol. 12 suppl. D, O271-4 (2005).
[8] J. Piskorski, P. Guzik, Filtering Poincaré plots, Comp. Meth. Sci. Tech. 11, 39-48 (2005).
[9] J. Piskorski, P. Guzik, Geometry of the Poincaré plot and its asymmetry in healthy adults. Phys. Meas. 28, 287-300 (2007).
[10] J.J. van Lieshout, K. Toska, E.J. van Lieshout, M. Eriksen, L. Walloe, K.H. Wesseling, Beat-to-beat noninvasive stroke volume from arterial pressure and Doppler ultrasound. Eur. J. Appl. Physiol. 90, 131-137 (2003).
[11] The animation described in the present paper can be downladed in various formats from
www.if.uz.zgora.pl/~jaropis/dyndesig.html
[12] N.M. Kaplan, Hypertension and atherosclerotic cardiovascular disease. In: Braunwald E, Zipes DP, Libby P. editors. Heart Disease. A Textbook of cardiovascular medicine, 6th Edition, p. 941-971. W.B. Saunders Company (2001).
[13] Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology, Heart rate variability. Standards of measurement, physiological interpretation, and clinical use. Eur. Heart J. 17, 354-381 (1996).
[14] R.E. Kleiger, P.K. Stein, J.T. Bigger Jr, Heart rate variability: measurement and clinical utility. Ann. Noninvasive Electrocardiol. 10, 88-101 (2005).
We apply the dynamic decomposition of Poincaré plots, which is a computationally intensive, visual method of analysing physiological time series, to the analysis of the interbeat interval variability, systolic blood pressure, stroke volume and total peripheral resistance which were simultaneously recorded from a patient with pheochromocytoma and rapid, repetitive haemodynamic changes over 6 hours. The resulting animation is analysed and interpreted. It is found that changes in total peripheral resistance usually precede those of other variables, and the magnitude of changes is greatest for this variable. It is demonstrated that the decomposition of Poincaré plots of multivariate signals can visualise both the order and the extent of ongoing instant changes.
Key words:
haemodynamics, heart rate variability, medical visualisation, pheochromocytoma, Poincaré plot
References:
[1] A.J.E. Seely, P.T. Macklem, Complex systems and the technology of variability analysis. Critic. Care 6, R367-84 (2004).
[2] M. Brennan, M. Palaniswami, P. Kamen, Do existing measures of Poincaré plot geometry reflect nonlinear features of Heart Rate Variability? IEEE Trans. Biomed. Eng. 48, 1342-1347 (2001).
[3] A. Voss, R. Schroeder, G. Trubner, M. Goering, H.R. Figulla, A. Schirdewan, Comparison of nonlinear methods symbolic dynamics, detrended fluctuation, and Poincaré plot analysis
in risk stratification in patients with dilated cardiomyopathy. Chaos 17, 015120-1-7 (2007).
[4] S. Guzzetti, M.G. Signorini, C. Cogliati, S. Mezzetti, A. Porta, S. Cerutti, A. Malliani, Non-linear dynamics and chaotic indices in heart rate variability of normal subjects and
heart-transplanted patients. Cardiovasc. Res. 31, 441-446 (1995).
[5] M. Costa, A.L. Goldberger, C K. Peng, Multiscale entropy analysis of biological signals. Phys. Rev. E 71, 021906-1- 18 (2005).
[6] P. Guzik, A. Wykretowicz, K.H. Wesseling, H. Wysocki, Adrenal pheochromocytoma associated with dramatic cyclic haemodynamic fluctuations. Int. J. Cardiol. 103, 351-353 (2005).
[7] P. Guzik, J. Piskorski, Decomposition of the commet-like Poincaré plot s of RR intervals. Folia Cardiol. 12 suppl. D, O271-4 (2005).
[8] J. Piskorski, P. Guzik, Filtering Poincaré plots, Comp. Meth. Sci. Tech. 11, 39-48 (2005).
[9] J. Piskorski, P. Guzik, Geometry of the Poincaré plot and its asymmetry in healthy adults. Phys. Meas. 28, 287-300 (2007).
[10] J.J. van Lieshout, K. Toska, E.J. van Lieshout, M. Eriksen, L. Walloe, K.H. Wesseling, Beat-to-beat noninvasive stroke volume from arterial pressure and Doppler ultrasound. Eur. J. Appl. Physiol. 90, 131-137 (2003).
[11] The animation described in the present paper can be downladed in various formats from
www.if.uz.zgora.pl/~jaropis/dyndesig.html
[12] N.M. Kaplan, Hypertension and atherosclerotic cardiovascular disease. In: Braunwald E, Zipes DP, Libby P. editors. Heart Disease. A Textbook of cardiovascular medicine, 6th Edition, p. 941-971. W.B. Saunders Company (2001).
[13] Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology, Heart rate variability. Standards of measurement, physiological interpretation, and clinical use. Eur. Heart J. 17, 354-381 (1996).
[14] R.E. Kleiger, P.K. Stein, J.T. Bigger Jr, Heart rate variability: measurement and clinical utility. Ann. Noninvasive Electrocardiol. 10, 88-101 (2005).
