Characterizing nonlinear heartbeat dynamics within a point process framework

Heartbeat intervals are known to have nonlinear and non-stationary dynamics. In this paper, we propose a nonlinear Volterra-Wiener expansion modeling of human heartbeat dynamics within a point process framework. Inclusion of second-order nonlinearity allows us to estimate dynamic bispectrum. The proposed probabilistic model was examined with two recorded heartbeat interval data sets. Preliminary results show that our model is beneficial to characterize the inherent nonlinearity of the heartbeat dynamics.

[1]  M. Korenberg,et al.  Orthogonal approaches to time-series analysis and system identification , 1991, IEEE Signal Processing Magazine.

[2]  D Verotta,et al.  Age and autonomic effects on interrelationships between lung volume and heart rate. , 1996, The American journal of physiology.

[3]  Maria G. Signorini,et al.  Nonlinear analysis of Heart Rate Variability signal for the characterization of Cardiac Heart Failure patients , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[4]  J. Richman,et al.  Physiological time-series analysis using approximate entropy and sample entropy. , 2000, American journal of physiology. Heart and circulatory physiology.

[5]  Thomas Schreiber,et al.  Testing for nonlinearity in unevenly sampled time series , 1998, chao-dyn/9804042.

[6]  Marimuthu Palaniswami,et al.  Distortion properties of the interval spectrum of IPFM generated heartbeats for heart rate variability analysis , 2001, IEEE Transactions on Biomedical Engineering.

[7]  V. Marmarelis.  Identification of nonlinear biological systems using laguerre expansions of kernels , 1993, Annals of Biomedical Engineering.

[8]  G Sugihara,et al.  Nonlinear control of heart rate variability in human infants. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[9]  Mauricio Barahona,et al.  Detection of nonlinear dynamics in short, noisy time series , 1996, Nature.

[10]  T. Seppänen,et al.  Physiological Background of the Loss of Fractal Heart Rate Dynamics , 2005, Circulation.

[11]  Otto Rompelman,et al.  The Measurement of Heart Rate Variability Spectra with the Help of a Personal Computer , 1982, IEEE Transactions on Biomedical Engineering.

[12]  C. Peng,et al.  What is physiologic complexity and how does it change with aging and disease? , 2002, Neurobiology of Aging.

[13]  V. Somers,et al.  Heart Rate Variability: , 2003, Journal of cardiovascular electrophysiology.

[14]  M. Wand.  Local Regression and Likelihood , 2001 .

[15]  Cahit Erkal,et al.  Normal heartbeat series are nonchaotic, nonlinear, and multifractal: new evidence from semiparametric and parametric tests. , 2009, Chaos.

[16]  E. Brown,et al.  A point-process model of human heartbeat intervals: new definitions of heart rate and heart rate variability. , 2005, American journal of physiology. Heart and circulatory physiology.

[17]  Madalena Costa,et al.  Multiscale entropy analysis of complex physiologic time series. , 2002, Physical review letters.

[18]  Emery N. Brown,et al.  A study of probabilistic models for characterizing human heart beat dynamics in autonomic blockade control , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[19]  Jeffrey M. Hausdorff,et al.  Fractal dynamics in physiology: Alterations with disease and aging , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[20]  Natalia M. Arzeno,et al.  Chaotic Signatures of Heart Rate Variability and Its Power Spectrum in Health, Aging and Heart Failure , 2009, PloS one.

[21]  A. Kikuchi,et al.  Nonlinear Analyses of Heart Rate Variability in Monochorionic and Dichorionic Twin Fetuses , 2007, Gynecologic and Obstetric Investigation.

[22]  Dirk Ramaekers,et al.  Approximate Entropy of Heart Rate Variability: Validation of Methods and Application in Heart Failure , 2001 .

[23]  K. Lutchen,et al.  Application of linear and nonlinear time series modeling to heart rate dynamics analysis , 1995, IEEE Transactions on Biomedical Engineering.

[24]  Silke Lange,et al.  Regular heartbeat dynamics are associated with cardiac health. , 2007, American journal of physiology. Regulatory, integrative and comparative physiology.

[25]  Chi-Sang Poon,et al.  Decrease of cardiac chaos in congestive heart failure , 1997, Nature.

[26]  F Atyabi,et al.  Two Statistical Methods for Resolving Healthy Individuals and Those with Congestive Heart Failure Based on Extended Self-similarity and a Recursive Method , 2006, Journal of biological physics.

[27]  O. Rössler.  An equation for continuous chaos , 1976 .

[28]  M. P. Griffin,et al.  Sample entropy analysis of neonatal heart rate variability. , 2002, American journal of physiology. Regulatory, integrative and comparative physiology.

[29]  Emery N. Brown,et al.  Computational Neuroscience: A Comprehensive Approach , 2022 .

[30]  Ki H. Chon,et al.  A Stochastic Nonlinear Autoregressive Algorithm Reflects Nonlinear Dynamics of Heart-Rate Fluctuations , 2002, Annals of Biomedical Engineering.

[31]  Guohua Pan,et al.  Local Regression and Likelihood , 1999, Technometrics.

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

[33]  R. Thuraisingham,et al.  On multiscale entropy analysis for physiological data , 2006 .

[34]  David T. Westwick,et al.  Explicit LeastSquares Methods , 2003 .

[35]  黄亚明.  PhysioBank , 2009 .

[36]  S. Van Huffel,et al.  Sensitivity of detrended fluctuation analysis applied to heart rate variability of preterm newborns , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[37]  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 .

[38]  Riccardo Barbieri,et al.  Characterizing Nonlinear Heartbeat Dynamics Within a Point Process Framework , 2008, IEEE Transactions on Biomedical Engineering.

[39]  A. G. Barnett,et al.  A time-domain test for some types of nonlinearity , 2005, IEEE Transactions on Signal Processing.

[40]  J. Strackee,et al.  Comparing Spectra of a Series of Point Events Particularly for Heart Rate Variability Data , 1984, IEEE Transactions on Biomedical Engineering.

[41]  Emery N. Brown,et al.  Assessment of Autonomic Control and Respiratory Sinus Arrhythmia Using Point Process Models of Human Heart Beat Dynamics , 2009, IEEE Transactions on Biomedical Engineering.

[42]  Daryl J. Daley,et al.  An Introduction to the Theory of Point Processes , 2013 .

[43]  Pablo Laguna,et al.  Improved heart rate variability signal analysis from the beat occurrence times according to the IPFM model , 2000, IEEE Transactions on Biomedical Engineering.

[44]  C. L. Nikias,et al.  Signal processing with higher-order spectra , 1993, IEEE Signal Processing Magazine.

[45]  David T. Westwick,et al.  Identification of Nonlinear Physiological Systems: Westwick/Identification of Nonlinear Physiological Systems , 2005 .

[46]  Michael J. Korenberg,et al.  Parallel cascade identification and kernel estimation for nonlinear systems , 2006, Annals of Biomedical Engineering.

[47]  Richard A. Heath,et al.  Nonlinear Dynamics: Techniques and Applications in Psychology , 2000 .

[48]  Emery N. Brown,et al.  Linear and nonlinear quantification of respiratory sinus arrhythmia during propofol general anesthesia , 2009, Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[49]  A L Goldberger,et al.  Physiological time-series analysis: what does regularity quantify? , 1994, The American journal of physiology.

[50]  K. Chon,et al.  A dual-input nonlinear system analysis of autonomic modulation of heart rate , 1996, IEEE Transactions on Biomedical Engineering.

[51]  S M Pincus,et al.  Approximate entropy as a measure of system complexity. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[52]  C. L. Nikias,et al.  Higher-order spectra analysis : a nonlinear signal processing framework , 1993 .

[53]  Metin Akay,et al.  Nonlinear Biomedical Signal Processing, Dynamic Analysis and Modeling , 2000 .

[54]  L. Glass.  Synchronization and rhythmic processes in physiology , 2001, Nature.

[55]  K. Sunagawa,et al.  Dynamic nonlinear vago-sympathetic interaction in regulating heart rate , 2005, Heart and Vessels.

[56]  James Theiler,et al.  Testing for nonlinearity in time series: the method of surrogate data , 1992 .

[57]  L. Amaral,et al.  Multifractality in human heartbeat dynamics , 1998, Nature.

[58]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[59]  Detrended Fluctuation Analysis (DFA) and R-R Interval variability: A new linear segmentation algorithm , 2006, 2006 Computers in Cardiology.

[60]  Emery N. Brown,et al.  Assessment of baroreflex control of heart rate during general anesthesia using a point process method , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[61]  Ki H. Chon,et al.  Nonlinear analysis of the separate contributions of autonomic nervous systems to heart rate variability using principal dynamic modes , 2004, IEEE Transactions on Biomedical Engineering.

[62]  A. Blasi,et al.  A Nonlinear Model of Cardiac Autonomic Control in Obstructive Sleep Apnea Syndrome , 2007, Annals of Biomedical Engineering.

[63]  C. Peng,et al.  Mosaic organization of DNA nucleotides. , 1994, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[64]  Emery N. Brown,et al.  Analysis of heartbeat dynamics by point process adaptive filtering , 2006, IEEE Transactions on Biomedical Engineering.

[65]  H. Stanley,et al.  Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. , 1995, Chaos.