Long- and short-time analysis of heartbeat sequences: Correlation with mortality risk in congestive heart failure patients

Physical Review E - Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics

Volumn 67, Issue 6, 2003, Pages 4-

  Allegrini, P ^a   Balocchi, R ^a   Chillemi, S ^b   Grigolini, P ^b,c,d   Hamilton, P ^e   Maestri, R ^f   Palatella, L ^d   Raffaelli, G ^g  

^a CNR   (Italy)

^b CNR Consiglio Nazionale delle Ricerche   (Italy)

^c UNIVERSITY OF NORTH TEXAS   (United States)

^d UNIVERSITY OF PISA   (Italy)

^e TEXAS WOMAN'S UNIVERSITY   (United States)

^f IRCCS   (Italy)

^g SISSA   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGICAL MODEL; BIOPHYSICS; CARDIOVASCULAR FUNCTION; HEART DISEASE; HEART FAILURE; HEART RATE; HUMAN; MORTALITY; MULTIVARIATE ANALYSIS; NONLINEAR SYSTEM; PATHOPHYSIOLOGY; PROCEDURES; RISK; SIGNAL PROCESSING; STATISTICAL MODEL; SURVIVAL; SYSTOLE; TIME;

BIOPHYSICS; CARDIOVASCULAR PHYSIOLOGICAL PHENOMENA; HEART DISEASES; HEART FAILURE; HEART RATE; HUMANS; MODELS, BIOLOGICAL; MODELS, STATISTICAL; MULTIVARIATE ANALYSIS; NONLINEAR DYNAMICS; RISK; SIGNAL PROCESSING, COMPUTER-ASSISTED; SURVIVAL ANALYSIS; SYSTOLE; TIME;

CARDIOVASCULAR SURGERY; CARDIOVASCULAR SYSTEM; COMPUTER SIMULATION; DISEASES; ELECTROCARDIOGRAPHY; PATIENT TREATMENT; PROBABILITY DISTRIBUTIONS; STATISTICAL METHODS;

CONGESTIVE HEART FAILURE; HEARTBEAT SEQUENCES; MORTALITY RISK; STATISTICAL PROPERTIES;

BIOMEDICAL ENGINEERING;

EID: 84929031163     PISSN: 1063651X     EISSN: None     Source Type: Journal    
DOI: 10.1103/PhysRevE.67.062901     Document Type: Article

References (14)

1. P. Allegrini, P. Grigolini, P. Hamilton, L. Palatella, and G. Raffaelli, Phys. Rev. E 65, 041926 (2002).
2. Available on CD-Rom: M. G. Signorini, R. Sassi, and S. Cerutti, in Proceedings of the 23th Annual Conference of the IEEE-EMBS, Istanbul, Turkey, 2001 (IEEE Press, Piscataway, NJ, 2001).
3. N. Scafetta, P. Hamilton, and P. Grigolini, Fractals 9, 193 (2001).
4. P. Grigolini, L. Palatella, and G. Raffaelli, Fractals 9, 439 (2001).
5. We assume that the signal of Fig. 22 of Ref. 1 is derived from that of Fig. 11 by adding short-time noise to it.
6. C. E. Shannon and W. Weaver, The Mathematical Theory of Communication (University of Illinois Press, Champaign, IL, 1963).
7. M. Annunziato and P. Grigolini, Phys. Lett. A 269, 31 (2000).
8. B. V. Gnedenko and A. N. Kolmogorov, Limit Distributions for Sums of Independent Random Variables (Addison-Wesley, Cambridge, MA, 1954).
9. Note that in literature concerning DNA sequences there are already models yielding the result of Eq. (16). Some authors 10 refer to this kind of model as a generalized Lévy (GL) process. Other authors 11 called this kind of model a copying mistake map (CMM). In a more recent work 12 the equivalence between GL and CMM processes has been carefully discussed. We note that there is a subtle difference between the model of this Brief Report and these earlier models, due to the fact that here we have a signal that is is the sum of a long-time memory component and a short-time noise, while in the earlier models the trajectory adopted either the random or the correlated prescription, with different probabilities.
10. S. V. Buldyrev, A. L. Goldberger, S. Havlin, C.-K. Peng, M. Simons, and H. E. Stanley, Phys. Rev. E 47, 4514 (1993).
11. P. Allegrini, M. Barbi, P. Grigolini, and B. J. West, Phys. Rev. E 52, 5281 (1995)
12. P. Allegrini, P. Grigolini, and B. J. West, Phys. Lett. A 211, 217 (1996).
13. P. Allegrini, M. Buiatti, P. Grigolini, and B. J. West, Phys. Rev. E 58, 3640 (1998).
14. M. Hollander and D. A. Wolfe, Nonparametric Statistical Methods (John Wiley and Sons, New York, 1973).