Application of dynamic point process models to cardiovascular control

The development of statistical models that accurately describe the stochastic structure of biological signals is a fast growing area in quantitative research. In developing a novel statistical paradigm based on Bayes' theorem applied to point processes, we are focusing our recent research on characterizing the physiological mechanisms involved in cardiovascular control. Results from a tilt table study point at our statistical framework as a valid model for the heart beat, as generated from complex mechanisms underlying cardiovascular control. The point process analysis provides new quantitative indices that could have important implications for research studies of cardiovascular and autonomic regulation and for monitoring of heart rate and heart rate variability measures in clinical settings.

[1]  Roger G. Mark,et al.  Circulatory response to passive and active changes in posture , 2003, Computers in Cardiology, 2003.

[2]  J. Leroy Folks,et al.  The Inverse Gaussian Distribution: Theory: Methodology, and Applications , 1988 .

[3]  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.

[4]  K H Wesseling,et al.  Initial blood pressure fall on stand up and exercise explained by changes in total peripheral resistance. , 1991, Journal of applied physiology.

[5]  R. Cohen,et al.  An Efficient Algorithm for Spectral Analysis of Heart Rate Variability , 1986, IEEE Transactions on Biomedical Engineering.

[6]  Jan Strackee,et al.  Spectrum of a series of point events, generated by the integral pulse frequency modulation model , 1985, Medical and Biological Engineering and Computing.

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

[8]  Emery N. Brown,et al.  The Time-Rescaling Theorem and Its Application to Neural Spike Train Data Analysis , 2002, Neural Computation.

[9]  Emery N. Brown,et al.  Dynamic Analysis of Neural Encoding by Point Process Adaptive Filtering , 2004, Neural Computation.

[10]  J. Taylor,et al.  Lesser vagal withdrawal during isometric exercise with age. , 1995, Journal of applied physiology.

[11]  J M Neilson,et al.  Autonomic mechanisms in the initial heart rate response to standing. , 1980, Journal of applied physiology: respiratory, environmental and exercise physiology.

[12]  R. Mark,et al.  Computational modeling of cardiovascular response to orthostatic stress. , 2002, Journal of applied physiology.

[13]  H. Stauss,et al.  Heart rate variability. , 2003, American journal of physiology. Regulatory, integrative and comparative physiology.

[14]  A. J. Dunning,et al.  Mechanisms of initial heart rate response to postural change. , 1982, The American journal of physiology.

[15]  Jianfeng Feng,et al.  Computational neuroscience , 1986, Behavioral and Brain Sciences.

[16]  Matthew A. Wilson,et al.  Dynamic Analyses of Information Encoding in Neural Ensembles , 2004, Neural Computation.

[17]  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 .

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

[19]  B. J. Sjöberg,et al.  Cardiac output and blood pressure during active and passive standing. , 1996, Clinical physiology.