Analysis Techniques

Chapter
First Online:
Part of the SpringerBriefs in Bioengineering book series (BRIEFSBIOENG)

Abstract

The introduction of TCD probes, together with the vascular unloading ABP technique, meant that since the 1980s it has been possible to obtain continuous and simultaneous measurements of both ABP and CBFV at high temporal resolution and at relatively low cost. This has resulted in a rich literature of analysis methods that attempt to understand the relationship between these two variables. The first studies mostly assumed that the relationship is univariate, linear and stationary; however, all of these assumptions have been challenged by later investigations. In particular, the role of blood gas levels is now known to be of considerable importance.

In this chapter, analysis methods will be presented, starting with the earliest, simplest methods, before exploring how later techniques have extended the analysis to non-linear, non-stationary and multivariate methods. For ease of presentation, analysis techniques are divided into those that are based in the time domain and those that are based in the frequency domain, although there is no fundamental theoretical difference between them.

Keywords

Impulse Response Step Response Cerebral Autoregulation Finite Impulse Response Filter Synchronization Index 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References

  1. Aaslid R, Lindegaard KF, Sorteberg W, Nornes H (1989) Cerebral autoregulation dynamics in humans. Stroke 20(1):45–52CrossRefGoogle Scholar
  2. Addison PS (2015) A review of wavelet transform time-frequency methods for NIRS-based analysis of cerebral autoregulation. IEEE Rev Biomed Eng 8:78–85CrossRefGoogle Scholar
  3. Angarita-Jaimes N, Kouchakpour H, Liu J, Panerai RB, Simpson DM (2014) Optimising the assessment of cerebral autoregulation from black box models. Med Eng Phys 36(5):607–612CrossRefGoogle Scholar
  4. Aoi MC, Matzuka BJ, Olufsen MS (2011) Toward online, noninvasive, nonlinear assessment of cerebral autoregulation. Conf Proc IEEE Eng Med Biol Soc 2011:2410–2413Google Scholar
  5. Bellapart J, Fraser JF (2009) Transcranial Doppler assessment of cerebral autoregulation. Ultrasound Med Biol 35(6):883–893CrossRefGoogle Scholar
  6. Birch AA, Dirnhuber MJ, Hartley-Davies R, Iannotti F, Neil-Dwyer G (1995) Assessment of autoregulation by means of periodic changes in blood pressure. Stroke 26(5):834–837CrossRefGoogle Scholar
  7. Brodie FG, Atkins ER, Robinson TG, Panerai RB (2009) Reliability of dynamic cerebral autoregulation measurement using spontaneous fluctuations in blood pressure. Clin Sci (Lond) 116(6):513–520CrossRefGoogle Scholar
  8. Chacon M, Nuñez N, Henríquez C, Panerai RB (2008) Unconstrained parameter estimation for assessment of dynamic cerebral autoregulation. Physiol Meas 29(10):1179–1193CrossRefGoogle Scholar
  9. Chiu CC, Yeh SJ (2001) Assessment of cerebral autoregulation using time-domain cross-correlation analysis. Comput Biol Med 31(6):471–480CrossRefGoogle Scholar
  10. Christ M, Noack F, Schroeder T, Hagmueller A, Koch R, May SA, Morgenstern U, Ragaller M, Steinmeier R (2007) Continuous cerebral autoregulation monitoring by improved cross-correlation analysis: comparison with the cuff deflation test. Intensive Care Med 33(2):246–254CrossRefGoogle Scholar
  11. Czosnyka M, Smielewski P, Kirkpatrick P, Menon DK, Pickard JD (1996) Monitoring of cerebral autoregulation in head-injured patients. Stroke 27(10):1829–1834CrossRefGoogle Scholar
  12. Deegan BM, Serrador JM, Nakagawa K, Jones E, Sorond FA, Olaighin G (2011) The effect of blood pressure calibrations and transcranial Doppler signal loss on transfer function estimates of cerebral autoregulation. Med Eng Phys 33(5):553–562CrossRefGoogle Scholar
  13. Diehl RR, Linden D, Lücke D, Berlit P (1998) Spontaneous blood pressure oscillations and cerebral autoregulation. Clin Auton Res 8(1):7–12CrossRefGoogle Scholar
  14. Gehalot P, Zhang R, Mathew A, Behbehani K (2005) Efficacy of using mean arterial blood pressure sequence for linear modeling of cerebral autoregulation. Conf Proc IEEE Eng Med Biol Soc 6:5619–5622Google Scholar
  15. Giller CA (1990) The frequency-dependent behavior of cerebral autoregulation. Neurosurgery 27(3):362–368CrossRefGoogle Scholar
  16. Giller CA, Iacopino DG (1997) Use of middle cerebral velocity and blood pressure for the analysis of cerebral autoregulation at various frequencies: the coherence index. Neurol Res 19(6):634–640Google Scholar
  17. Giller CA, Müller M (2003) Linearity and non-linearity in cerebral hemodynamics. Med Eng Phys 25(8):633–646CrossRefGoogle Scholar
  18. Gommer ED, Shijaku E, Mess WH, Reulen JP (2010) Dynamic cerebral autoregulation: different signal processing methods without influence on results and reproducibility. Med Biol Eng Comput 48(12):1243–1250CrossRefGoogle Scholar
  19. Hu K, Peng CK, Czosnyka M, Zhao P, Novak V (2008) Nonlinear assessment of cerebral autoregulation from spontaneous blood pressure and cerebral blood flow fluctuations. Cardiovasc Eng 8(1):60–71CrossRefGoogle Scholar
  20. Huang et al., Proc Royal Soc A, Huang NE, Shen Z, Long S, Wu M, Shih H, Zheng Q, Yen N, Tung C, Liu H (1998) The empirical mode decomposition and Hilbert spectrum for non-linear and non-stationary time series analysis. Proc R Soc Lond A 454:903–995Google Scholar
  21. Jachan M, Reinhard M, Spindeler L, Hetzel A, Schelter B, Timmer J (2009) Parametric versus nonparametric transfer function estimation of cerebral autoregulation from spontaneous blood-pressure oscillations. Cardiovasc Eng 9(2):72–82CrossRefGoogle Scholar
  22. Kantz and Schreiber (2004) Nonlinear time series analysis, CUP, 2nd edn.Google Scholar
  23. Katura T, Tanaka N, Obata A, Sato H, Maki A (2006) Quantitative evaluation of interrelations between spontaneous low-frequency oscillations in cerebral hemodynamics and systemic cardiovascular dynamics. Neuroimage 31(4):1592–1600CrossRefGoogle Scholar
  24. Kostoglou K, Debert CT, Poulin MJ, Mitsis GD (2014) Nonstationary multivariate modeling of cerebral autoregulation during hypercapnia. Med Eng Phys 36(5):592–600CrossRefGoogle Scholar
  25. Latka M, Turalska M, Glaubic-Latka M, Kolodziej W, Latka D, West BJ (2005) Phase dynamics in cerebral autoregulation. Am J Physiol Heart Circ Physiol 289(5):H2272–H2279CrossRefMATHGoogle Scholar
  26. Liu Y, Allen R (2002) Analysis of dynamic cerebral autoregulation using an ARX model based on arterial blood pressure and middle cerebral artery velocity simulation. Med Biol Eng Comput 40(5):600–605CrossRefGoogle Scholar
  27. Liu Y, Birch AA, Allen R (2003) Dynamic cerebral autoregulation assessment using an ARX model: comparative study using step response and phase shift analysis. Med Eng Phys 25(8):647–653CrossRefGoogle Scholar
  28. Liu J, Simpson MD, Yan J, Allen R (2010) Tracking time-varying cerebral autoregulation in response to changes in respiratory PaCO2. Physiol Meas 31(10):1291–1307CrossRefGoogle Scholar
  29. Liu J, Simpson DM, Kouchakpour H, Panerai RB, Chen J, Gao S, Zhang P, Wu X (2014) Rapid pressure-to-flow dynamics of cerebral autoregulation induced by instantaneous changes of arterial CO2. Med Eng Phys 36(12):1636–1643CrossRefGoogle Scholar
  30. Liu X, Czosnyka M, Donnelly J, Budohoski KP, Varsos GV, Nasr N, Brady KM, Reinhard M, Hutchinson PJ, Smielewski P (2015) Comparison of frequency and time domain methods of assessment of cerebral autoregulation in traumatic brain injury. J Cereb Blood Flow Metab 35(2):248–256CrossRefGoogle Scholar
  31. Meel-van den Abeelen AS, van Beek AH, Slump CH, Panerai RB, Claassen JA (2014) Transfer function analysis for the assessment of cerebral autoregulation using spontaneous oscillations in blood pressure and cerebral blood flow. Med Eng Phys 36(5):563–575Google Scholar
  32. Meel-van den Abeelen AS, Simpson DM, Wang LJ, Slump CH, Zhang R, Tarumi T, Rickards CA, Payne S, Mitsis GD, Kostoglou K, Marmarelis V, Shin D, Tzeng YC, Ainslie PN, Gommer E, Müller M, Dorado AC, Smielewski P, Yelicich B, Puppo C, Liu X, Czosnyka M, Wang CY, Novak V, Panerai RB, Claassen JA (2014) Between-centre variability in transfer function analysis, a widely used method for linear quantification of the dynamic pressure-flow relation: the CARNet study. Med Eng Phys 36(5):620–627Google Scholar
  33. Mitsis GD, Zhang R, Levine BD, Marmarelis VZ (1985) Cerebral hemodynamics during orthostatic stress assessed by nonlinear modeling. J Appl Physiol 101(1):354–366Google Scholar
  34. Mitsis GD, Zhang R, Levine BD, Marmarelis VZ (2002) Modeling of nonlinear physiological systems with fast and slow dynamics. II. Application to cerebral autoregulation. Ann Biomed Eng 30(4):555–565CrossRefGoogle Scholar
  35. Mitsis GD, Poulin MJ, Robbins PA, Marmarelis VZ (2004) Nonlinear modeling of the dynamic effects of arterial pressure and CO2 variations on cerebral blood flow in healthy humans. IEEE Trans Biomed Eng 51(11):1932–1943CrossRefGoogle Scholar
  36. Müller MW, Osterreich M (2014) A comparison of dynamic cerebral autoregulation across changes in cerebral blood flow velocity for 200 s. Front Physiol 5:327Google Scholar
  37. Noack F, Christ M, May SA, Steinmeier R, Morgenstern U (2007) Assessment of dynamic changes in cerebral autoregulation. Biomed Tech (Berl) 52(1):31–36CrossRefGoogle Scholar
  38. Novak V, Yang AC, Lepicovsky L, Goldberger AL, Lipsitz LA, Peng CK (2004) Multimodal pressure-flow method to assess dynamics of cerebral autoregulation in stroke and hypertension. Biomed Eng Online 3(1):39CrossRefGoogle Scholar
  39. Panerai RB (2009) Transcranial Doppler for evaluation of cerebral autoregulation. Clin Auton Res 19(4):197–211CrossRefGoogle Scholar
  40. Panerai RB (2014) Nonstationarity of dynamic cerebral autoregulation. Med Eng Phys 36(5):576–584CrossRefGoogle Scholar
  41. Panerai RB, Deverson ST, Mahony P, Hayes P, Evans DH (1999a) Effects of CO2 on dynamic cerebral autoregulation measurement. Physiol Meas 20(3):265–275CrossRefGoogle Scholar
  42. Panerai RB, Dawson SL, Potter JF (1999b) Linear and nonlinear analysis of human dynamic cerebral autoregulation. Am J Physiol 277(3 Pt 2):H1089–H1099Google Scholar
  43. Panerai RB, Simpson DM, Deverson ST, Mahony P, Hayes P, Evans DH (2000) Multivariate dynamic analysis of cerebral blood flow regulation in humans. IEEE Trans Biomed Eng 47(3):419–423CrossRefGoogle Scholar
  44. Panerai RB, Dawson SL, Eames PJ, Potter JF (2001) Cerebral blood flow velocity response to induced and spontaneous sudden changes in arterial blood pressure. Am J Physiol Heart Circ Physiol 280(5):H2162–H2174Google Scholar
  45. Panerai RB, Eames PJ, Potter JF (2003) Variability of time-domain indices of dynamic cerebral autoregulation. Physiol Meas 24(2):367–381CrossRefGoogle Scholar
  46. Panerai RB, Chacon M, Pereira R, Evans DH (2004) Neural network modelling of dynamic cerebral autoregulation: assessment and comparison with established methods. Med Eng Phys 26(1):43–52CrossRefGoogle Scholar
  47. Panerai RB, Eames PJ, Potter JF (2006) Multiple coherence of cerebral blood flow velocity in humans. Am J Physiol Heart Circ Physiol 291(1):H251–H259CrossRefGoogle Scholar
  48. Panerai RB, Sammons EL, Smith SM, Rathbone WE, Bentley S, Potter JF, Samani NJ (2008) Continuous estimates of dynamic cerebral autoregulation: influence of non-invasive arterial blood pressure measurements. Physiol Meas 29(4):497–513CrossRefGoogle Scholar
  49. Peng T, Rowley AB, Ainslie PN, Poulin MJ, Payne SJ (2008) Multivariate system identification for cerebral autoregulation. Ann Biomed Eng 36(2):308–320CrossRefGoogle Scholar
  50. Riera J, Cabañas F, Serrano JJ, Bravo MC, López-Ortego P, Sánchez L, Madero R, Pellicer A (2014) New time-frequency method for cerebral autoregulation in newborns: predictive capacity for clinical outcomes. J Pediatr 165(5):897–902.e1Google Scholar
  51. Rosengarten B, Kaps M (2002) Peak systolic velocity Doppler index reflects most appropriately the dynamic time course of intact cerebral autoregulation. Cerebrovasc Dis 13(4):230–234CrossRefGoogle Scholar
  52. Steinmeier R, Hofmann RP, Bauhuf C, Hübner U, Fahlbusch R (2002a) Continuous cerebral autoregulation monitoring by cross-correlation analysis. J Neurotrauma 19(10):1127–1138CrossRefGoogle Scholar
  53. Steinmeier R, Bauhuf C, Hübner U, Hofmann RP, Fahlbusch R (2002b) Continuous cerebral autoregulation monitoring by cross-correlation analysis: evaluation in healthy volunteers. Crit Care Med 30(9):1969–1975CrossRefGoogle Scholar
  54. Taylor JA, Tan CO, Hamner JW (2014) Assessing cerebral autoregulation via oscillatory lower body negative pressure and projection pursuit regression. J Vis Exp (94)Google Scholar
  55. Tiecks FP, Lam AM, Aaslid R, Newell DW (1995) Comparison of static and dynamic cerebral autoregulation measurements. Stroke 26(6):1014–1019CrossRefGoogle Scholar
  56. Wang SY, Tang MX (2004) Exact confidence interval for magnitude-squared coherence estimates. IEEE Sig Proc Lett 11(3):326–329CrossRefGoogle Scholar
  57. Zernikow B, Michel E, Kohlmann G, Steck J, Schmitt RM, Jorch G (1994) Cerebral autoregulation of preterm neonates–a non-linear control system? Arch Dis Child Fetal Neonatal Ed 70(3):F166–F173CrossRefGoogle Scholar
  58. Zhang R, Zuckerman JH, Giller CA, Levine BD (1998) Transfer function analysis of dynamic cerebral autoregulation in humans. Am J Physiol 274(1 Pt 2):H233–H241Google Scholar

Copyright information

© The Author(s) 2016

Authors and Affiliations

  1. 1.Department of Engineering ScienceUniversity of OxfordOxfordUK

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