Annals of Biomedical Engineering

, Volume 39, Issue 1, pp 260–276

Dynamic Assessment of Baroreflex Control of Heart Rate During Induction of Propofol Anesthesia Using a Point Process Method

  • Zhe Chen
  • Patrick L. Purdon
  • Grace Harrell
  • Eric T. Pierce
  • John Walsh
  • Emery N. Brown
  • Riccardo Barbieri
Article

Abstract

In this article, we present a point process method to assess dynamic baroreflex sensitivity (BRS) by estimating the baroreflex gain as focal component of a simplified closed-loop model of the cardiovascular system. Specifically, an inverse Gaussian probability distribution is used to model the heartbeat interval, whereas the instantaneous mean is identified by linear and bilinear bivariate regressions on both the previous R−R intervals (RR) and blood pressure (BP) beat-to-beat measures. The instantaneous baroreflex gain is estimated as the feedback branch of the loop with a point-process filter, while the \(\hbox{RR}\to\hbox{BP}\) feedforward transfer function representing heart contractility and vasculature effects is simultaneously estimated by a recursive least-squares filter. These two closed-loop gains provide a direct assessment of baroreflex control of heart rate (HR). In addition, the dynamic coherence, cross bispectrum, and their power ratio can also be estimated. All statistical indices provide a valuable quantitative assessment of the interaction between heartbeat dynamics and hemodynamics. To illustrate the application, we have applied the proposed point process model to experimental recordings from 11 healthy subjects in order to monitor cardiovascular regulation under propofol anesthesia. We present quantitative results during transient periods, as well as statistical analyses on steady-state epochs before and after propofol administration. Our findings validate the ability of the algorithm to provide a reliable and fast-tracking assessment of BRS, and show a clear overall reduction in baroreflex gain from the baseline period to the start of propofol anesthesia, confirming that instantaneous evaluation of arterial baroreflex control of HR may yield important implications in clinical practice, particularly during anesthesia and in postoperative care.

Keywords

Baroreflex control Baroreflex sensitivity Heart rate variability Hemodynamics Point processes Adaptive filters Volterra series Closed-loop feedback control Cardiovascular system 

References

  1. 1.
    Barbieri, R., and E. N. Brown. Analysis of heart beat dynamics by point process adaptive filtering. IEEE Trans. Biomed. Eng. 53:4–12, 2006.CrossRefPubMedGoogle Scholar
  2. 2.
    Barbieri, R., E. C. Matten, A. A. Alabi, and E. N. Brown. A point-process model of human heartbeat intervals: new definitions of heart rate and heart rate variability. Am J. Physiol. Heart Circ. Physiol. 288:H424–H435, 2005.CrossRefPubMedGoogle Scholar
  3. 3.
    Barbieri, R., G. Parati, and J. P. Saul. Closed- versus open-loop assessment of heart rate baroreflex. IEEE Eng. Med. Biol. 20: 33–42, 2001.CrossRefGoogle Scholar
  4. 4.
    Barbieri, R., R. A. Waldmann, V. Di Virgilio, J. K. Triedman, A. M. Bianchi, S. Cerutti, and J. P. Saul. Continuous quantification of baroreflex and respiratory control of heart rate by use of bivarate autoregressive techniques. Ann. Noninvasive Electrocardiol. 3:264–277, 1996.CrossRefGoogle Scholar
  5. 5.
    Baselli, G., S. Cerutti, S. Civardi, A. Malliani, and M. Pagani. Cardiovascular variability signals: towards the identification of a closed-loop model of the neural control mechanisms. IEEE Trans. Biomed. Eng. 35:1033–1046, 1988.CrossRefPubMedGoogle Scholar
  6. 6.
    Baselli, G., M. Porta, O. Rimoldi, M. Pagani, and S. Cerutti. Spectral decomposition in multichannel recordings based on multivariate parametric identification. IEEE Trans. Biomed. Eng. 44:1092–1101, 1997.CrossRefPubMedGoogle Scholar
  7. 7.
    Betzel, J., R. Mukkamala, G. Baselli, and K. H. Chon. Modeling and disentangling physiological mechanisms: linear and nonlinear identification techniques for analysis of cardiovascular regulation. Philos. Trans. R. Soc. A 367:1377–1391, 2009.CrossRefGoogle Scholar
  8. 8.
    Bristow, J. D., C. Prys-Roberts, A. Fisher, T. G. Pickering, and P. Sleight. Effects of anesthesia on baroreflex control of heart rate in man. Anesthesiology 31: 422–428, 1969.CrossRefPubMedGoogle Scholar
  9. 9.
    Brown, E. N., R. Barbieri, U. T. Eden, and L. M. Frank. Likelihood methods for neural data analysis. In: Computational Neuroscience: A Comprehensive Approach, edited by J. Feng. Boca Raton, MA: CRC Press, 2003, pp. 253–286.Google Scholar
  10. 10.
    Carlson, J. T., J. A. Hedner, J. Sellgren, M. Elam, and B. G. Wallin. Depressed baroreflex sensitivity in patients with obstructive sleep apnea. Am. J. Respir. Crit. Care Med. 154:1490–1496, 1996.PubMedGoogle Scholar
  11. 11.
    Carter, J. A., T. N. S. Clarke, C. Prys-Roberts, and K. R. Spelina. Restoration of baroreflex control of heart rate during recovery from anaesthesia. Br. J. Anaesth. 58: 415–421, 1986.CrossRefPubMedGoogle Scholar
  12. 12.
    Chen, Z., E. N. Brown, and R. Barbieri. A study of probabilistic models for characterizing human heart beat dynamics in autonomic blockade control. In: Proceedings of the IEEE ICASSP, 2008, pp. 481–484.Google Scholar
  13. 13.
    Chen, Z., E. N. Brown, and R. Barbieri. A point process approach to assess dynamic baroreflex gain. In: Proceedings of the Computers in Cardiology, 2008, pp. 805–808.Google Scholar
  14. 14.
    Chen, Z., E. N. Brown, and R. Barbieri. A unified point process framework for assessing heartbeat dynamics and cardiovascular control. In: Proceedings of the IEEE 35th Northeast Bioengineering Conference, 2009, pp. 1–2.Google Scholar
  15. 15.
    Chen, Z., E. N. Brown, and R. Barbieri. Assessment of autonomic control and respiratory sinus arrhythmia using point process models of human heart beat dynamics. IEEE Trans. Biomed. Eng. 56:1791–1802, 2009.PubMedGoogle Scholar
  16. 16.
    Chen, Z., E. N. Brown, and R. Barbieri. Characterizing nonlinear heartbeat dynamics within a point process framework. IEEE Trans. Biomed. Eng. 57:1335–1347, 2010.PubMedGoogle Scholar
  17. 17.
    Chen, Z., P. L. Purdon, E. T. Pierce, G. Harrell, E. N. Brown, and R. Barbieri. Assessment of baroreflex control of heart rate during general anesthesia using a point process method. In: Proceedings of the IEEE ICASSP, 2009, pp. 333–336.Google Scholar
  18. 18.
    Chon, K. H., T. J. Mullen, and R. J. Cohen. A dual-input nonlinear system analysis of autonomic modulation of heart rate. IEEE Trans. Biomed. Eng. 43:530–540, 1995.CrossRefGoogle Scholar
  19. 19.
    Clayton, R. H., A. J. Bowman, and A. Murray. Measurement of baroreflex gain from heart rate and blood pressure spectra. Physiol. Meas. 16:131–139, 1995.CrossRefPubMedGoogle Scholar
  20. 20.
    Cullen, P. M., M. Turtle, C. Prys-Roberts, W. L. Way, and J. Dye. Effect of propofol anesthesia on baroreflex activity in humans. Anesth. Analg. 66:115–120, 1987.Google Scholar
  21. 21.
    De Boer, R. W., J. M. Karemaker, and J. Strackee. Relationships between short-term blood-pressure fluctuations and heart-rate variability in resting subjects: a spectral analysis approach. Med. Biol. Eng. Comput. 23:352–358, 1985.CrossRefPubMedGoogle Scholar
  22. 22.
    Eckberg, D. L. Nonlinearities of the human carotid baroreceptor-cardiac reflex. Circ. Res. 47:208–216, 1980.PubMedGoogle Scholar
  23. 23.
    Eckberg, D. L. Arterial baroreflexes and cardiovascular modeling. Cardiovasc. Eng. 8:5–13, 2008.CrossRefPubMedGoogle Scholar
  24. 24.
    Eckberg, D. L., S. W. Harkins, J. M. Fritsch, G. E. Musgrave, and D. F. Gardner. Baroreflex control of plasma norepinephrine and heart period in healthy subjects and diabetic patients. J. Clin. Invest. 78:366–374, 1986.CrossRefPubMedGoogle Scholar
  25. 25.
    Eden, U. T., L. M. Frank, V. Solo, and E. N. Brown. Dynamic analyses of neural encoding by point process adaptive filtering. Neural Comput. 16:971–998, 2004.CrossRefPubMedGoogle Scholar
  26. 26.
    Faes, L., G. Nollo, and K. H. Chon. Assessment of Granger causality by nonlinear model identification: application to short-term cardiovascular variability. Ann. Biomed. Eng. 36:381–395, 2007.CrossRefGoogle Scholar
  27. 27.
    Faes, L., A. Porta, R. Cucino, S. Cerutti, R. Antolini, and G. Nollo. Causal transfer function analysis to describe closed loop interactions between cardiovascular and cardiorespiratory variability signals. Biol. Cybern. 90:390–399, 2004.CrossRefPubMedGoogle Scholar
  28. 28.
    Feld, J., W. Hoffman, C. Paisansathan, H. Park, and R. C. Ananda. Autonomic activity during dexmedetomidine or fentanyl infusion with desflurane anesthesia. J. Clin. Anesth. 19:30–36, 2003.CrossRefGoogle Scholar
  29. 29.
    Fietze, I., D. Romberg, M. Glos, S. Endres, H. Theres, C. Witt, and V. K. Somers. Effects of positive-pressure ventilation on the spontaneous baroreflex in healthy subjects. J. Appl. Physiol. 96:1155–1160, 2004.CrossRefPubMedGoogle Scholar
  30. 30.
    Haykin, S. Adaptive Filter Theory, 4th ed. Upper Saddle River, NJ: Prentice Hall, 2001.Google Scholar
  31. 31.
    Hughson, R. L., L. Quintin, G. Annat, Y. Yamamoto, and C. Gharib. Spontaneous baroreflex by sequence and power spectral methods in humans. Clin. Physiol. 13:663–676, 1993.CrossRefPubMedGoogle Scholar
  32. 32.
    Jo, J. A., A. Blasi, E. M. Valladares, R. Juarez, A. Baydur, and M. C. K. Khoo. A nonlinear model of cardiac autonomic control in obstructive sleep apnea syndrome. Ann. Biomed. Eng. 35:1425–1443, 2007.CrossRefPubMedGoogle Scholar
  33. 33.
    Lu, S., K. H. Ju, and K. H. Chon. A new algorithm for linear and nonlinear ARMA model parameter estimation using afne geometry. IEEE Trans. Biomed. Eng. 48(10):1116–1124, 2001.CrossRefPubMedGoogle Scholar
  34. 34.
    Mainardi, L. T. On the quantification of heart rate variability spectral parameters using time-frequency and time-varying methods. Philos. Trans. R. Soc. A 367:255–275, 2009.CrossRefGoogle Scholar
  35. 35.
    Mainardi, L. T., A. M. Bianchi, R. Furlan, S. Piazza, R. Barbieri, V. de Virgilio, A. Malliani, and S. Cerutti. Multivariate time-variant identification of cardiovascular variability signals: a beat-to-beat spectral parameter estimation in vasovagal syncope. IEEE Trans. Biomed. Eng. 44(10):978–988, 1997.CrossRefPubMedGoogle Scholar
  36. 36.
    Marmarelis, V. Z. Nonlinear Dynamic Modeling of Physiological Systems. New York: Wiley, 2004.Google Scholar
  37. 37.
    Nagasaki, G., M. Tanaka, and T. Nishikawa. The recovery profile of baroreflex control of heart rate after isoflurane or sevoflurane anesthesia in humans. Anesth. Analg. 93:1127–1131, 2001.CrossRefPubMedGoogle Scholar
  38. 38.
    Nikias, C., and A. P. Petropulu. Higher Order Spectra Analysis: A Non-Linear Signal Processing Framework. Upper Saddle River, NJ: Prentice Hall, 1993.Google Scholar
  39. 39.
    Nollo, G., A. Porta, L. Faes, M. Del Greco, M. Disertori, and F. Ravelli. Causal linear parametric model for baroreflex gain assessment in patients with recent myocardial infarction. Am. J. Physiol. Heart Circ. Physiol. 280:H1830–H1839, 2001.PubMedGoogle Scholar
  40. 40.
    Parati, G., M. DiRienzo, and G. Mancia. Dynamic modulation of baroreflex sensitivity in health and disease. Ann. N. Y. Acad. Sci. 940:469–487, 2001.CrossRefPubMedGoogle Scholar
  41. 41.
    Peden, C. J., A. H. Cloote, N. Stratford, and C. Prys-Roberts. The effect of intravenous dexmedetomidine premedication on the dose requirement of propofol to induce loss of consciousness in patients receiving alfentanil. Anaesthesia 56:408–413, 2001.CrossRefPubMedGoogle Scholar
  42. 42.
    Peng, C.-K., S. Havlin, H. E. Stanley, and A. L., Goldberger. Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos 5:82–87, 1995.CrossRefPubMedGoogle Scholar
  43. 43.
    Pinna, G. D. Assessing baroreflex sensitivity by the transfer function method: what are we really measuring? J. Appl. Physiol. 102:1310–1311, 2007.CrossRefPubMedGoogle Scholar
  44. 44.
    Pinna, G. D., and R. Maestri. New criteria for estimating baroreflex sensitivity using the transfer function method. J. Med. Biol. Eng. Comput. 40:79–84, 2001.CrossRefGoogle Scholar
  45. 45.
    Poon, C.-S., and C. K. Merrill. Decrease of cardiac chaos in congestive heart failure. Nature 389:492–495, 1997.CrossRefPubMedGoogle Scholar
  46. 46.
    Porta, A., F. Aletti, F. Vallais, and G. Baselli. Multimodal signal processing for the analysis of cardiovascular variability. Philos. Trans. R. Soc. A 367:391-409, 2009.CrossRefGoogle Scholar
  47. 47.
    Porta, A., R. Furlan, O. Rimoldi, M. Pagani, A. Malliani, and P. van de Borne. Quantifying the strength of linear causal coupling in closed loop interacting cardiovascular variability signals. Biol. Cybern. 86:241–251, 2002.CrossRefPubMedGoogle Scholar
  48. 48.
    Purdon, P. L., E. T. Pierce, G. Bonmassar, J. Walsh, G. Harrell, J. Kwo, D. Deschler, M. Barlow, R. C. Merhar, C. Lamus, C. M. Mullaly, M. Sullivan, S. Maginnis, D. Skoniecki, H. Higgins, and E. N. Brown. Simultaneous electroencephalography and functional magnetic resonance imaging of general anesthesia. Ann. N. Y. Acad. Sci. 1157:61–70, 2009.CrossRefPubMedGoogle Scholar
  49. 49.
    Sato, M., M. Tanaka, S. Umehara, and T. Nishikawa. Baroreflex control of heart rate during and after propofol infusion in humans. Br. J. Anaesth. 94:577–581, 2005.CrossRefPubMedGoogle Scholar
  50. 50.
    Saul, J. P., R. D. Berger, P. Albrecht, S. P. Stein, M. H. Chen, and R. J. Cohen. Transfer function analysis of the circulation: unique insights into cardiovascular regulation. Am. J. Physiol. Heart. Circ. Physiol. 261:H1231–H1245, 1991.Google Scholar
  51. 51.
    Schetzen, M. The Volterra and Wiener Theories of Nonlinear Systems. New York: Wiley, 1980.Google Scholar
  52. 52.
    Schnider, T. W., C. F. Minto, P. L. Gambus, C. Andresen, D. B. Goodale, S. L. Shafer, and E. J. Youngs. The influence of method of administration and covariates on the pharmacokinetics of propofol in adult volunteers. Anesthesiology 88:1170–1182,1998.CrossRefPubMedGoogle Scholar
  53. 53.
    Schnider, T. W., C. F. Minto, S. L. Shafer, P. L. Gambus, C. Andresen, D. B. Goodale, and E. J. Youngs. The influence of age on propofol pharmacodynamics. Anesthesiology 89:67–72, 1999.Google Scholar
  54. 54.
    Sellgren, J., H. Ejnell, M. Elam, J. Pontén, and B. G. Wallin. Sympathetic muscle nerve activity, peripheral blood flows, and baroreceptor reflexes in humans during propofol anesthesia and surgery. Anesthesiology 80:534–544, 1994.CrossRefPubMedGoogle Scholar
  55. 55.
    Shafer, A., V. A. Doze, S. L. Shafer, and P. F. White. Pharmacokinetics and pharmacodynamics of propofol infusions during general anesthesia. Anesthesiology 69:348–356, 1988.CrossRefPubMedGoogle Scholar
  56. 56.
    Tanaka, M., G. Nagaski, and T. Nishikawa. Moderate hypothermia depresses arterial baroreflex control of heart rate during, and delays it recovery after, general anesthesia in humans. Anesthesiology 95:51–55, 2001.CrossRefPubMedGoogle Scholar
  57. 57.
    Tanaka, M., and T. Nishikawa. The concentration-dependent effects of general anesthesia on spontaneous baroreflex indices and their correlations with pharmacological gains. Anesth. Analg. 100:1325–1332, 2005.CrossRefPubMedGoogle Scholar
  58. 58.
    Tsoulkas, V., P. Koukoulas, and N. Kalouptsidis. Identification of input output bilinear systems using cumulants. IEEE Trans. Signal Process. 49:2753–2761, 2001.CrossRefGoogle Scholar
  59. 59.
    Xiao, X., T. J. Mullen, and R. Mukkamala. System identication: a multi-signal approach for probing neural cardiovascular regulation. Physiol. Meas. 26:R41–R71, 2005.CrossRefPubMedGoogle Scholar
  60. 60.
    Wang, H., K. Ju, and K. H. Chon. Closed-loop nonlinear system identification via the vector optimal parameter search algorithm: application to heart rate baroreflex control. Med. Eng. Phys. 29:505–515, 2007.CrossRefPubMedGoogle Scholar
  61. 61.
    Zhao, H., W. A. Cupples, K. Ju, and K. H. Chon. Time-varying causal coherence function and its application to renal blood pressure and blood flow data. IEEE Trans. Biomed. Eng. 54:2142–2150, 2007.CrossRefPubMedGoogle Scholar
  62. 62.
    Zhao, H., S. Lu, R. Zou, K. Ju, and K. H. Chon. Estimation of time-varying coherence function using time-varying transfer functions. Ann. Biomed. Eng. 33:1582–1594, 2005.CrossRefPubMedGoogle Scholar
  63. 63.
    Zou, R., H. Wang, and K. H. Chon. A robust time-varying identification algorithm using basis functions. Ann. Biomed. Eng. 31:840–853, 2003.CrossRefPubMedGoogle Scholar

Copyright information

© Biomedical Engineering Society 2010

Authors and Affiliations

  • Zhe Chen
    • 1
    • 3
  • Patrick L. Purdon
    • 1
  • Grace Harrell
    • 1
  • Eric T. Pierce
    • 1
  • John Walsh
    • 1
  • Emery N. Brown
    • 1
    • 2
    • 3
  • Riccardo Barbieri
    • 1
  1. 1.Neuroscience Statistics Research LaboratoryMassachusetts General Hospital, Harvard Medical SchoolBostonUSA
  2. 2.Harvard-MIT Division of Health Science and TechnologyCambridgeUSA
  3. 3.Department of Brain and Cognitive SciencesMassachusetts Institute of TechnologyCambridgeUSA

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