Ordinal pattern statistics for the assessment of heart rate variability

The recognition of all main features of a healthy heart rhythm (the so-called sinus rhythm) is still one of the biggest challenges in contemporary cardiology. Recently the interesting physiological phenomenon of heart rate asymmetry has been observed. This phenomenon is related to unbalanced contributions of heart rate decelerations and accelerations to heart rate variability. In this paper we apply methods based on the concept of ordinal pattern to the analysis of electrocardiograms (inter-peak intervals) of healthy subjects in the supine position. This way we observe new regularities of the heart rhythm related to the distribution of ordinal patterns of lengths 3 and 4.

[1]  Miguel A. F. Sanjuán,et al.  Permutation complexity of spatiotemporal dynamics , 2010 .

[2]  José Amigó,et al.  Permutation Complexity in Dynamical Systems , 2010 .

[3]  Jos Amig Permutation Complexity in Dynamical Systems: Ordinal Patterns, Permutation Entropy and All That , 2010 .

[4]  Jaroslaw Piskorski,et al.  Heart rate asymmetry by Poincaré plots of RR intervals , 2006, Biomedizinische Technik. Biomedical engineering.

[5]  Niels Wessel,et al.  Classifying cardiac biosignals using ordinal pattern statistics and symbolic dynamics , 2012, Comput. Biol. Medicine.

[6]  Alberto Porta,et al.  Assessment of cardiac autonomic modulation during graded head-up tilt by symbolic analysis of heart rate variability. , 2007, American journal of physiology. Heart and circulatory physiology.

[7]  B. Pompe,et al.  Permutation entropy: a natural complexity measure for time series. , 2002, Physical review letters.

[8]  Marek Malik,et al.  Heart rate deceleration runs for postinfarction risk prediction. , 2012, Journal of electrocardiology.

[9]  Gaoxiang Ouyang,et al.  Ordinal pattern based similarity analysis for EEG recordings , 2010, Clinical Neurophysiology.

[10]  M Palaniswami,et al.  Defining asymmetry in heart rate variability signals using a Poincaré plot , 2009, Physiological measurement.

[11]  J. Piskorski,et al.  Geometry of the Poincaré plot of RR intervals and its asymmetry in healthy adults , 2007, Physiological measurement.

[12]  T. Schreiber,et al.  Surrogate time series , 1999, chao-dyn/9909037.

[13]  A. Malliani,et al.  Heart rate variability. Standards of measurement, physiological interpretation, and clinical use , 1996 .

[14]  E. Migliaro,et al.  Asymmetrical properties of heart rate variability in type 1 diabetes , 2010, Clinical Autonomic Research.

[15]  J. Piskorski,et al.  The structure of heart rate asymmetry: deceleration and acceleration runs , 2011, Physiological measurement.

[16]  G. Keller,et al.  Entropy of interval maps via permutations , 2002 .

[17]  D Geue,et al.  Temporal asymmetries of short-term heart period variability are linked to autonomic regulation. , 2008, American journal of physiology. Regulatory, integrative and comparative physiology.

[18]  Marimuthu Palaniswami,et al.  Do existing measures of Poincare plot geometry reflect nonlinear features of heart rate variability? , 2001, IEEE Transactions on Biomedical Engineering.

[19]  A. Porta,et al.  Progressive decrease of heart period variability entropy-based complexity during graded head-up tilt. , 2007, Journal of applied physiology.

[20]  G. Breithardt,et al.  Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. , 1996 .