Modelling and disentangling physiological mechanisms: linear and nonlinear identification techniques for analysis of Cardiovascular regulation

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences

Volumn 367, Issue 1892, 2009, Pages 1377-1391

  Batzel, Jerry ^a   Baselli, Giuseppe ^b   Mukkamala, Ramakrishna ^c   Chon, Ki H ^d  

^a UNIVERSITY OF GRAZ   (Austria)

^b POLITECNICO DI MILANO   (Italy)

^c Michigan State University   (United States)

^d St Francis Hospital The Heart Center   (United States)

Author keywords

Cardiovascular system; Closed loop control; Closed loop identification; Modelling; Nonlinear identification; Sensitivity analysis

Indexed keywords

CARDIOVASCULAR SYSTEM; CLOSED LOOP CONTROL SYSTEMS; NONLINEAR ANALYSIS; PHYSIOLOGICAL MODELS; SENSITIVITY ANALYSIS;

BASIC CONCEPTS; CARDIOVASCULAR REGULATION; CLINICAL APPLICATION; CLOSED-LOOP CONTROL; CLOSED-LOOP IDENTIFICATION; COMPLEX CONTROL; COMPUTATIONAL POWER; EXPERIMENTAL DATA; GREY-BOX MODELLING; MATHEMATICAL MODELLING; MODEL VALIDATION; MODELLING; MODELLING TECHNIQUES; NONLINEAR EXTENSION; NONLINEAR IDENTIFICATION; NONLINEAR IDENTIFICATIONS; PHYSIOLOGICAL MECHANISMS; ROBUST DIAGNOSTICS;

IDENTIFICATION (CONTROL SYSTEMS);

ADRENERGIC SYSTEM; ALGORITHM; ARTICLE; BIOLOGICAL MODEL; CARDIOVASCULAR DISEASE; CARDIOVASCULAR SYSTEM; CHOLINERGIC SYSTEM; COMPUTER PROGRAM; HUMAN; METABOLISM; PHYSIOLOGY; PRESSORECEPTOR REFLEX; REGRESSION ANALYSIS; SENSITIVITY AND SPECIFICITY; SIGNAL PROCESSING; STATISTICAL MODEL; THEORETICAL MODEL; TIME;

ALGORITHMS; BAROREFLEX; CARDIOVASCULAR DISEASES; CARDIOVASCULAR SYSTEM; HUMANS; LEAST-SQUARES ANALYSIS; MODELS, BIOLOGICAL; MODELS, STATISTICAL; MODELS, THEORETICAL; PARASYMPATHETIC NERVOUS SYSTEM; SENSITIVITY AND SPECIFICITY; SIGNAL PROCESSING, COMPUTER-ASSISTED; SOFTWARE; SYMPATHETIC NERVOUS SYSTEM; TIME FACTORS;

EID: 65549146854     PISSN: 1364503X     EISSN: None     Source Type: Journal    
DOI: 10.1098/rsta.2008.0266     Document Type: Article

References (32)

1. Akselrod, S., Gordon, D., Ubel, F. A., Shannon, D. C., Berger, A. C. and Cohen, R. J. 1981 Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat-to-beat cardiovascular control. Science 213, 220-222. (doi:10.1126/science.6166045)
2. Baselli, G. et al.1994 A model for the assessment of heart period and arterial pressure variability interactions and of respiration influences. Med. Biol. Eng. Comput. 32, 143-152. (doi:10.1007 /BF02518911)
3. Batzel, J. J., Kappel, F., Tran, H. T. and Schneditz, D. 2006 Cardiovascular and respiratory systems: modeling, analysis, and control. Philadelphia, PA: SIAM.
4. Burth, M., Verghese, G. C. and V́elez-Reyes, M. 1999 Subset selection for improved parameter estimation in on-line identification of a synchronous generator. IEEE Trans. Power Syst. 14, 218-225. (doi:10.1109/59.744536)
5. Catchpole, E. A., Morgan, B. J. T. and Freeman, S. N. 1998 Estimation in parameter redundant models. Biometrika 85, 462-468. (doi:10.1093/biomet/85.2.462)
6. Chen, X. and Mukkamala, R. 2008 Selective quantification of the cardiac sympathetic and parasympathetic nervous systems by multi-signal analysis of cardio-respiratory variability. Am. J. Physiol. 294, H362-H371.
7. Chen, X., Kim, J., Sala-Mercado, J. A., Hammond, R. L., Elahi, R. I., Scislo, T., Swamy, G., O'Leary, D. S. and Mukkamala, R. 2008 Estimation of the total peripheral resistance baroreflex impulse response from spontaneous hemodynamic variability. Am. J. Physiol. 294, H293-H301. (doi:10.1152/ajpheart.00852.2007)
8. Chon, K. H., Mullen, T. J. and Cohen, R. J. 1996 A dual-input nonlinear system analysis of autonomic modulation of heart rate. IEEE Trans. Biomed. Eng. 43, 530-544. (doi:10.1109/10.488800)
9. Cobelli, C. and DiStefano III, J. J. 1980 Parameter and structural identifiability concepts and ambiguities: a critical review and analysis. Am. J. Physiol. 239, R7-R24.
10. Fink, M., Batzel, J. J. and Tran, H. T. 2008 A respiratory system model: parameter estimation and sensitivity analysis. Cardiovasc. Eng. 8, 120-134. (doi:10.1007/s10558-007-9051-7)
11. Geweke, J. 1982 Measurement of linear dependence and feedback between multiple time series. J. Am. Stat. As Soc. 77, 304-313. (doi:10.2307/2287238)
12. Gimenez, O., Viallefont, A., Catchpole, E. A., Choquet, R. and Morgan, B. J. T. 2004 Methods for investigating parameter redundancy. Anim. Biodiv. Conserv. 27, 561-572.
13. Granger, C. W. J. 1963 Economic processes involving feedback information and control. Inform. Control 6, 28-48. (doi:10.1016/S0019-9958 (63) 90092-5)
14. Guyton, A. C. and Harris, J. W. 1951 Pressoreceptor-autonomic oscillation; a probable cause of vasomotor waves. Am. J. Physiol. 165, 158-166.
15. Heldt, T., Long, B., Verghese, G. C., Szolovits, P. and Mark, R. G. 2006 Integrating data, models, and reasoning in critical care. In Engineering in Medicine and Biology Society, 2006 EMBS 28th Annu. Int. Conf. of the IEEE, vol. 1, pp. 350-353.
16. Henon, M. 1976 A two-dimensional map with strange attractor. Commun. Math. Phys. 50, 69-77. (doi:10.1007/BF01608556)
17. Hyndman, B. W., Kitney, R. I. and Sayers, B. M. 1971 Spontaneous rhythms in physiological control systems. Nature 233, 339-341. (doi:10.1038/233339a0)
18. Koepchen, H. P. 1984 History of studies and concepts of blood pressure waves. In Mechanisms of blood pressure waves (eds K. Miyakawa, H. P. Koepchen and C. Polosa), pp. 3-27. New York, NY: Springer.
19. Levy, M. N. 1971 Sympathetic-parasympathetic interactions in the heart. Circ. Res. 29, 437-445.
20. Ljung, L. 1999 System identification: theory for the user. Englewood Cliffs, NJ: Prentice-Hall.
21. Lu, S., Ju, K. and Chon, K. H. 2001 A new algorithm for linear and nonlinear ARMA model parameter estimation using affine geometry. IEEE Trans. Biomed. Eng. 48, 1116-1124. (doi:10. 1109/10.951514)
22. Moon, T. K. and Stirling, W. C. 2000 Mathematical methods and algorithms for signal processing. Upper Saddle River, NJ: Prentice Hall.
23. Mukkamala, R., Toska, K. and Cohen, R. J. 2003 Noninvasive identification of the total peripheral resistance baroreflex. Am. J. Physiol. 284, H947-H959.
24. Mukkamala, R., Kim, J., Li, Y., Sala-Mercado, J., Hammond, R. L., Scislo, T. and O'Leary, D. S. 2006 Estimation of arterial and cardiopulmonary total peripheral resistance baroreflex gain values: validation by chronic arterial baroreceptor denervation. Am. J. Physiol. 290, H1830-H1836. (doi:10.1152/ajpheart.00898.2005)
25. Porta, G., Aletti, F., Vallais, F. and Baselli, G. 2009 Multimodal signal processing for the analysis of cardiovascular variability. Phil. Trans. R. Soc. A 367, 391-409. (doi:10.1098/rsta.2008.0229)
26. Saul, J. P., Berger, R. D., Chen, M. H. and Cohen, R. J. 1989 Transfer function analysis of autonomic regulation. II. Respiratory sinus arrhythmia. Am. J. Physiol. 256, H153-H161.
27. Thomaseth, K. and Cobelli, C. 1999 Generalized sensitivity functions in physiological system identification. Ann. Biomed. Eng. 27, 607-616. (doi:10.1114/1.207)
28. V́elez-Reyes, M. and Verghese, G. C. 1995 Subset selection in identification, and application to speed and parameter estimation for induction machines. In Proc. 4th IEEE Conf. on Control Applications, pp. 991-997.
29. Wang, H., Ju, K. and Chon, K. H. 2007 Closed-loop nonlinear system identification via the vector optimal parameter search algorithm: application to heart rate baroreflex control. Med. Eng. Phys. 29, 505-515. (doi:10.1016/j.medengphy.2006.06.013)
30. Xiao, X., Mukkamala, R., Sheynberg, N., Grenon, S. M., Ehrman, M. D., Mullen, T. J., Ramsdell, C. D., Williams, G. H. and Cohen, R. J. 2004 Effects of simulated microgravity on closed-loop cardiovascular regulation and orthostatic intolerance: analysis by means of system identification. J. Appl. Physiol. 96, 489-497. (doi:10.1152/japplphysiol.00602.2003)
31. Xiao, X., Mullen, T. J. and Mukkamala, R. 2005 System identification: a multi-signal approach for probing neural cardiovascular regulation. Physiol. Measure. 26, R41-R71. (doi:10.1088/0967-3334/26/3/R01)
32. Zou, R., Wang, H. and Chon, K. H. 2003 A robust time-varying identification algorithm using basis functions. Ann. Biomed. Eng. 31, 840-853. (doi:10.1114/1.1584683)