Poincaré Plot for Heart Rate Variability

Heart is the most important part in the body of living
organisms. It affects and is affected by any factor in the body.
Therefore, it is a good detector for all conditions in the body. Heart
signal is a non-stationary signal; thus, it is utmost important to study
the variability of heart signal. The Heart Rate Variability (HRV) has
attracted considerable attention in psychology, medicine and has
become important dependent measure in psychophysiology and
behavioral medicine. The standards of measurements, physiological
interpretation and clinical use for HRV that are most often used were
described in many researcher papers, however, remain complex
issues are fraught with pitfalls. This paper presents one of the nonlinear
techniques to analyze HRV. It discusses many points like, what
Poincaré plot is and how Poincaré plot works; also, Poincaré plot's
merits especially in HRV. Besides, it discusses the limitation of
Poincaré cause of standard deviation SD1, SD2 and how to overcome
this limitation by using complex correlation measure (CCM). The
CCM is most sensitive to changes in temporal structure of the
Poincaré plot as compared toSD1 and SD2.

• Mazhar B. Tayel
• Eslam I. AlSaba

• Heart rate variability
• chaotic system
• Poincaré
• variance
• standard deviation
• complex correlation measure.

[1] Malik M., Camm A.J. (eds.): Heart Rate Variability, Armonk, N.Y.
Futura Pub. Co. Inc., 1995.
[2] Hon EH, Lee ST. Electronic evaluations of the fetal heart rate patterns
preceding fetal death, further observations. Am J Obstet Gynae 1965.
[3] Ewing DJ, Martyn CN, Young RJ, Clarke BF. The value of
cardiovascular autonomic function tests: 10 years’ experience in
diabetes. Diabetes Care 1985.
[4] Malik M, Farrell T, Cripps T, Camm AJ. Heart rate variability in
relation to prognosis after myocardial infarction: selection of optimal
processing techniques. Eur Heart J 1989.
[5] Casolo G, Balli E, Taddei T, et al. Decreased spontaneous heart rate
variability on congestive cardiac failure.Am J Cardiol 1989.
[6] Marangoni S, Scalvini S, Mai R, et al. Heart rate variability assessment
in patients with mitral valve prolapse syndrome. Am J Noninvas
Cardiol 1993.
[7] Dougherty CM, Burr RL. Comparison of heart rate variability in
survivors and nonsurvivors of sudden cardiac arrest. Am J Cardiol 1992.
[8] Takens F 1981 Detecting strange attractors in turbulence Springer
Lecture Notes in Mathematics vol 898, pp 366–81
[9] Claudia Lerma, Oscar Infante, Hector Perez-Grovas and Marco V.Jose.
Poincaré plot indexes of heart rate variability capture dynamic
adaptations after haemodialysis in chronic renal failure patients. Clinical
Physiology & Functional Imaging (2003) 23, pp72–80.
[10] J. Piskorski and P. Guzik. Filtering poincar e plots. Computation
Methods in Science and Technology, June 2005.
[11] Karmakar C, Khandoker A, Gubbi J, Palaniswami M: Complex
Correlation Measure: a novel descriptor for Poincaré plot. BioMedical
Engineering OnLine 2009
[12] Rydberg A, Karlsson M, Hornsten R, Wiklund U. Can Analysis of Heart
Rate Variability Predict Arrhythmia in Children with Fontan
Circulation? Pediatr Cardiol 2008, 29:50-55.
[13] Mazhar B. Tayel, Eslam I. AlSaba. "Robust and Sensitive Method of
Lyapunov Exponent for Heart Rate Variability". International Journal of
Biomedical Engineering and Science (IJBES), Vol. 2, No. 3, July 2015.
pp 31 – 48