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Abstract

Each breath is not generated de novo; rather, the ventilatory pattern is a continuous oscillation in which the next breath is related to the present one; and being biologic, the ventilatory pattern varies. Further, the responsiveness of respiration to sensory input is dynamic because neural mechanisms scale afferent input. Thus, ventilatory pattern variability (VPV) has deterministic properties, which may vary in health and disease. We have developed analytical tools to distinguish and assess linear and nonlinear sources of VPV. Surrogate data sets obtained by shuffling the original data while preserving its amplitude distribution and autocorrelation function and, thus, preserving linear properties embedded within the original data are used to distinguish various sources and types of VPV. Differences in mutual information and sample entropy of VPV between original and surrogate data sets reflect nonlinear deterministic properties of the original data set. We have applied these analytic techniques to assess breathing pattern before and after vagotomy, cerebral ischemia, and lung injury. Deterministic variability decreased following each of these interventions. Finally, our approach can be applied to rhythmic biological signals.

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References

  1. Osler W (1903) On the education value of the medical society. Yale Med J 9(10):325–336

    Google Scholar 

  2. Hon EH, Lee ST (1963) Electronic evaluation of the fetal heart rate. VIII. Patterns preceding fetal death, further observations. Am J Obstet Gynecol 87:814–826

    PubMed  CAS  Google Scholar 

  3. Hon EH, Lee ST (1963) The fetal electrocardiogram. I. The electrocardiogram of the dying fetus. Am J Obstet Gynecol 87:804–813

    PubMed  CAS  Google Scholar 

  4. Baekey DM, Dick TE, Paton JF (2008) Pontomedullary transection attenuates central respiratory modulation of sympathetic discharge, heart rate and the baroreceptor reflex in the in situ rat preparation. Exp Physiol 93(7):803–816

    Article  PubMed  Google Scholar 

  5. Bloomfield DM et al (2001) Comparison of spontaneous vs. metronome-guided breathing on assessment of vagal modulation using RR variability. Am J Physiol Heart Circ Physiol 280(3):H1145–H1150

    PubMed  CAS  Google Scholar 

  6. Dhingra RR et al (2011) Vagal-dependent nonlinear variability in the respiratory pattern of anesthetized, spontaneously breathing rats. J Appl Physiol 111(1):272–284

    Article  PubMed  CAS  Google Scholar 

  7. Dick TE et al (2009) Cardio-respiratory coupling depends on the pons. Respir Physiol Neurobiol 168(1–2):76–85

    Article  PubMed  Google Scholar 

  8. Grossman P, Taylor EW (2007) Toward understanding respiratory sinus arrhythmia: relations to cardiac vagal tone, evolution and biobehavioral functions. Biol Psychol 74(2):263–285

    Article  PubMed  Google Scholar 

  9. Grossman P, Wilhelm FH, Spoerle M (2004) Respiratory sinus arrhythmia, cardiac vagal control, and daily activity. Am J Physiol Heart Circ Physiol 287(2):H728–H734

    Article  PubMed  CAS  Google Scholar 

  10. Hou L et al (2009) Presynaptic modulation of tonic and respiratory inputs to cardiovagal motoneurons by substance P. Brain Res 1284:31–40

    Article  PubMed  CAS  Google Scholar 

  11. Lombardi F et al (1987) Heart rate variability as an index of sympathovagal interaction after acute myocardial infarction. Am J Cardiol 60(16):1239–1245

    Article  PubMed  CAS  Google Scholar 

  12. Pyetan E, Akselrod S (2004) A theoretical appraisal of the dependence of respiratory sinus arrhythmia on gradual vagal blockade. Methods Inf Med 43(1):52–55

    PubMed  CAS  Google Scholar 

  13. Goldberger AL et al (2002) Fractal dynamics in physiology: alterations with disease and aging. Proc Natl Acad Sci USA 99(Suppl 1):2466–2472

    Article  PubMed  Google Scholar 

  14. Goldberger AL, Peng CK, Lipsitz LA (2002) What is physiologic complexity and how does it change with aging and disease? Neurobiol Aging 23(1):23–26

    Article  PubMed  Google Scholar 

  15. Peng CK et al (2002) Quantifying fractal dynamics of human respiration: age and gender effects. Ann Biomed Eng 30(5):683–692

    Article  PubMed  CAS  Google Scholar 

  16. Tobin MJ et al (1983) Breathing patterns. 2. Diseased subjects. Chest 84(3):286–294

    Article  PubMed  CAS  Google Scholar 

  17. Tobin MJ et al (1983) Breathing patterns. 1. Normal subjects. Chest 84(2):202–205

    Article  PubMed  CAS  Google Scholar 

  18. Tobin MJ et al (1988) Variability of resting respiratory drive and timing in healthy subjects. J Appl Physiol 65(1):309–317

    PubMed  CAS  Google Scholar 

  19. Brack T, Jubran A, Tobin MJ (2002) Dyspnea and decreased variability of breathing in patients with restrictive lung disease. Am J Respir Crit Care Med 165(9):1260–1264

    Article  PubMed  Google Scholar 

  20. Kuratomi Y et al (1985) Variability of breath-by-breath tidal volume and its characteristics in normal and diseased subjects. Ventilatory monitoring with electrical impedance pneumography. Jpn J Med 24(2):141–149

    Article  PubMed  CAS  Google Scholar 

  21. Loveridge B et al (1984) Breathing patterns in patients with chronic obstructive pulmonary disease. Am Rev Respir Dis 130(5):730–733

    PubMed  CAS  Google Scholar 

  22. Loveridge B et al (1986) Alteration in breathing pattern with progression of chronic obstructive pulmonary disease. Am Rev Respir Dis 134(5):930–934

    PubMed  CAS  Google Scholar 

  23. Cherniack NS, Longobardo G, Evangelista CJ (2005) Causes of Cheyne-Stokes respiration. Neurocrit Care 3(3):271–279

    Article  PubMed  CAS  Google Scholar 

  24. Cherniack NS, Longobardo GS (1973) Cheyne-Stokes breathing. An instability in physiologic control. N Engl J Med 288(18):952–957

    Article  PubMed  CAS  Google Scholar 

  25. Cherniack NS et al (1966) Periodic breathing in dogs. J Appl Physiol 21(6):1847–1854

    PubMed  CAS  Google Scholar 

  26. Longobardo GS, Cherniack NS, Fishman AP (1966) Cheyne-Stokes breathing produced by a model of the human respiratory system. J Appl Physiol 21(6):1839–1846

    PubMed  CAS  Google Scholar 

  27. Casas-Mendez LF et al (2011) Biot’s breathing in a woman with fatal familial insomnia: is there a role for noninvasive ventilation? J Clin Sleep Med 7(1):89–91

    PubMed  Google Scholar 

  28. Perini R, Veicsteinas A (2003) Heart rate variability and autonomic activity at rest and during exercise in various physiological conditions. Eur J Appl Physiol 90(3–4):317–325

    Article  PubMed  Google Scholar 

  29. Schuhmann RE, Hoff HE (1986) Central origin vs. reflex feedback in the respiratory heart rate relationship. Ann Biomed Eng 14(6):543–546

    Article  PubMed  CAS  Google Scholar 

  30. Webber CL Jr, Speck DF (1981) Experimental Biot periodic breathing in cats: effects of changes in PiO2 and PiCO2. Respir Physiol 46(3):327–344

    Article  PubMed  Google Scholar 

  31. Wijdicks EF (2007) Biot’s breathing. J Neurol Neurosurg Psychiatry 78(5):512–513

    Article  PubMed  Google Scholar 

  32. Delisle S et al (2012) Effect of ventilatory variability on occurrence of central apneas. Respir Care 58(5):745–753

    Article  Google Scholar 

  33. Khoo MC, Wang W, Chalacheva P (2011) Monitoring ultradian changes in cardiorespiratory control in obstructive sleep apnea syndrome. Conf Proc IEEE Eng Med Biol Soc 2011: 1487–1490

    PubMed  Google Scholar 

  34. Yamauchi M et al (2011) Differences in breathing patterning during wakefulness in patients with mixed apnea-dominant vs obstructive-dominant sleep apnea. Chest 140(1):54–61

    Article  PubMed  Google Scholar 

  35. Guilleminault C et al (2001) Variability of respiratory effort in relation to sleep stages in normal controls and upper airway resistance syndrome patients. Sleep Med 2(5):397–405

    Article  PubMed  CAS  Google Scholar 

  36. Khoo MC (2000) Determinants of ventilatory instability and variability. Respir Physiol 122(2–3):167–182

    Article  PubMed  CAS  Google Scholar 

  37. Stein MB et al (1995) Irregular breathing during sleep in patients with panic disorder. Am J Psychiatry 152(8):1168–1173

    PubMed  CAS  Google Scholar 

  38. Pfaltz MC et al (2010) Physical activity and respiratory behavior in daily life of patients with panic disorder and healthy controls. Int J Psychophysiol 78(1):42–49

    Article  PubMed  Google Scholar 

  39. Grassi M et al (2012) Baseline respiratory parameters in panic disorder: a meta-analysis. J Affect Disord 146(2):158–173

    Article  PubMed  Google Scholar 

  40. Brouillette RT, Weese-Mayer DE, Hunt CE (1990) Breathing control disorders in infants and children. Hosp Pract (Off Ed) 25(8):82–85, 88, 93–96 passim

    Google Scholar 

  41. Gaultier C, Gallego J (2008) Neural control of breathing: insights from genetic mouse models. J Appl Physiol 104(5):1522–1530

    Article  PubMed  CAS  Google Scholar 

  42. Glaze DG et al (1987) Rett’s syndrome: characterization of respiratory patterns and sleep. Ann Neurol 21(4):377–382

    Article  PubMed  CAS  Google Scholar 

  43. Katz DM et al (2009) Breathing disorders in Rett syndrome: progressive neurochemical dysfunction in the respiratory network after birth. Respir Physiol Neurobiol 168(1–2):101–108

    Article  PubMed  CAS  Google Scholar 

  44. Lin HY et al (2007) Polysomnographic characteristics in patients with Prader-Willi syndrome. Pediatr Pulmonol 42(10):881–887

    Article  PubMed  Google Scholar 

  45. Patwari PP et al (2010) Congenital central hypoventilation syndrome and the PHOX2B gene: a model of respiratory and autonomic dysregulation. Respir Physiol Neurobiol 173(3):322–335

    Article  PubMed  CAS  Google Scholar 

  46. Sforza E et al (1991) Sleep and breathing abnormalities in a case of Prader-Willi syndrome. The effects of acute continuous positive airway pressure treatment. Acta Paediatr Scand 80(1):80–85

    Article  PubMed  CAS  Google Scholar 

  47. Silvestri JM, Weese-Mayer DE (1996) Respiratory control disorders in infancy and childhood. Curr Opin Pediatr 8(3):216–220

    Article  PubMed  CAS  Google Scholar 

  48. Weese-Mayer DE et al (2010) An official ATS clinical policy statement: congenital central hypoventilation syndrome: genetic basis, diagnosis, and management. Am J Respir Crit Care Med 181(6):626–644

    Article  PubMed  CAS  Google Scholar 

  49. Weese-Mayer DE et al (2008) Congenital central hypoventilation syndrome (CCHS) and sudden infant death syndrome (SIDS): kindred disorders of autonomic regulation. Respir Physiol Neurobiol 164(1–2):38–48

    Article  PubMed  CAS  Google Scholar 

  50. Weese-Mayer DE et al (2008) Familial dysautonomia: frequent, prolonged and severe hypoxemia during wakefulness and sleep. Pediatr Pulmonol 43(3):251–260

    Article  PubMed  Google Scholar 

  51. Weese-Mayer DE et al (2008) Autonomic dysregulation in young girls with Rett Syndrome during nighttime in-home recordings. Pediatr Pulmonol 43(11):1045–1060

    Article  PubMed  Google Scholar 

  52. Weese-Mayer DE et al (2006) Autonomic nervous system dysregulation: breathing and heart rate perturbation during wakefulness in young girls with Rett syndrome. Pediatr Res 60(4):443–449

    Article  PubMed  Google Scholar 

  53. Dick TE et al (2012) Linking inflammation, cardiorespiratory variability, and neural control in acute inflammation via computational modeling. Front Physiol 3:222

    Article  PubMed  Google Scholar 

  54. Namas R et al (2011) Sepsis: something old, something new, and a systems view. J Crit Care 27(3):314.e1–314.e11

    Article  Google Scholar 

  55. Chan GS, Middleton PM, Lovell NH (2011) Photoplethysmographic variability analysis in critical care – current progress and future challenges. Conf Proc IEEE Eng Med Biol Soc 2011:5507–5510

    PubMed  Google Scholar 

  56. Tobin MJ et al (1983) Validation of respiratory inductive plethysmography in patients with pulmonary disease. Chest 83(4):615–620

    Article  PubMed  CAS  Google Scholar 

  57. Jacono F, De Georgia MA, Wilson CG, Dick TE, Loparo KA (2010) Data acquisition and complex systems analysis in critical care: developing the intensive care unit of the future. J Healthc Eng 1(3):337–356

    Article  Google Scholar 

  58. Webber CL Jr (2005) The meaning and measurement of physiologic variability? Crit Care Med 33(3):677–678

    Article  PubMed  Google Scholar 

  59. Fishman M et al (2012) A method for analyzing temporal patterns of variability of a time series from Poincare plots. J Appl Physiol 113(2):297–306

    Article  PubMed  Google Scholar 

  60. Sammon MP, Bruce EN (1991) Vagal afferent activity increases dynamical dimension of respiration in rats. J Appl Physiol 70(4):1748–1762

    PubMed  CAS  Google Scholar 

  61. Pincus SM, Goldberger AL (1994) Physiological time-series analysis: what does regularity quantify? Am J Physiol 266(4 Pt 2):H1643–H1656

    PubMed  CAS  Google Scholar 

  62. Richman JS, Lake DE, Moorman JR (2004) Sample entropy. Methods Enzymol 384:172–184

    Article  PubMed  Google Scholar 

  63. Richman JS, Moorman JR (2000) Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol 278(6):H2039–H2049

    PubMed  CAS  Google Scholar 

  64. Kaffashi F et al (2008) The effect of time delay on Approximate & Sample Entropy calculations. Phys Nonlinear Phenom 237:3069–3074

    Article  Google Scholar 

  65. Schreiber T, Schmitz A (2000) Surrogate time series. Phys Nonlinear Phenom 142:346–382

    Article  Google Scholar 

  66. Theiler J et al (1992) Testing for nonlinearity in time-series – the method of surrogate data. Phys Nonlinear Phenom 58:77–94

    Article  Google Scholar 

  67. Dutschmann M, Dick TE (2012) Pontine mechanisms of respiratory control. In: Mitchell GS et al (eds) Comprehensive physiology. Wiley/American Physiological Society, Bethesda, MD, pp 2443–2469

    Google Scholar 

  68. Koo BB et al (2010) Ventilatory patterning in a mouse model of stroke. Respir Physiol Neurobiol 172(3):129–135

    Article  PubMed  Google Scholar 

  69. Waggener TB et al (1984) Strength and cycle time of high-altitude ventilatory patterns in unacclimatized humans. J Appl Physiol 56(3):576–581

    PubMed  CAS  Google Scholar 

  70. Waggener TB et al (1984) Apnea duration is related to ventilatory oscillation characteristics in newborn infants. J Appl Physiol 57(2):536–544

    PubMed  CAS  Google Scholar 

  71. Bruce EN, Daubenspeck JA (1995) Mechanism and analysis of ventilatory stability. In: Dempsey JA, Pack AI (eds) Regulation of breathing. Marcel Dekker, New York, pp 285–313

    Google Scholar 

  72. Hudgel DW et al (1998) Instability of ventilatory control in patients with obstructive sleep apnea. Am J Respir Crit Care Med 158(4):1142–1149

    Article  PubMed  CAS  Google Scholar 

  73. Oku Y, Dick TE (1992) Phase resetting of the respiratory cycle before and after unilateral pontine lesion in cat. J Appl Physiol 72(2):721–730

    PubMed  CAS  Google Scholar 

  74. Poon CS, Barahona M (2001) Titration of chaos with added noise. Proc Natl Acad Sci USA 98(13):7107–7112

    Article  PubMed  CAS  Google Scholar 

  75. Samara Z et al (2009) Effects of inspiratory loading on the chaotic dynamics of ventilatory flow in humans. Respir Physiol Neurobiol 165(1):82–89

    Article  PubMed  Google Scholar 

  76. Wysocki M et al (2006) Chaotic dynamics of resting ventilatory flow in humans assessed through noise titration. Respir Physiol Neurobiol 153(1):54–65

    Article  PubMed  Google Scholar 

  77. Straus C et al (2011) Effects of maturation and acidosis on the chaos-like complexity of the neural respiratory output in the isolated brainstem of the tadpole, Rana esculenta. Am J Physiol Regul Integr Comp Physiol 300(5):R1163–R1174

    Article  PubMed  CAS  Google Scholar 

  78. Mangin L et al (2011) Ventilatory chaos is impaired in carotid atherosclerosis. PLoS One 6(1):e16297

    Article  PubMed  CAS  Google Scholar 

  79. Barthe JY, Clarac F (1997) Modulation of the spinal network for locomotion by substance P in the neonatal rat. Exp Brain Res 115(3):485–492

    Article  PubMed  CAS  Google Scholar 

  80. Telgkamp P et al (2002) Long-term deprivation of substance P in PPT-A mutant mice alters the anoxic response of the isolated respiratory network. J Neurophysiol 88(1):206–213

    PubMed  CAS  Google Scholar 

  81. Freitas US, Letellier C, Aguirre LA (2009) Failure in distinguishing colored noise from chaos using the “noise titration” technique. Phys Rev E Stat Nonlin Soft Matter Phys 79(3 Pt 2): 035201

    Article  PubMed  Google Scholar 

  82. Jacono FJ et al (2011) Lung and brainstem cytokine levels are associated with breathing pattern changes in a rodent model of acute lung injury. Respir Physiol Neurobiol 178(3):429–438

    Article  PubMed  CAS  Google Scholar 

  83. Bien MY et al (2004) Breathing pattern variability: a weaning predictor in postoperative patients recovering from systemic inflammatory response syndrome. Intensive Care Med 30(2):241–247

    Article  PubMed  Google Scholar 

  84. Casaseca-de-la-Higuera P, Martin-Fernandez M, Alberola-Lopez C (2006) Weaning from mechanical ventilation: a retrospective analysis leading to a multimodal perspective. IEEE Trans Biomed Eng 53(7):1330–1345

    Article  PubMed  Google Scholar 

  85. Giraldo BF et al (2004) Study of the respiratory pattern variability in patients during weaning trials. Conf Proc IEEE Eng Med Biol Soc 6:3909–3912

    PubMed  CAS  Google Scholar 

  86. Ibrahim LH et al (2008) A measure of ventilatory variability at wake-sleep transition predicts sleep apnea severity. Chest 134(1):73–78

    Article  PubMed  Google Scholar 

  87. Miyata M et al (2004) A short daytime test using correlation dimension for respiratory movement in OSAHS. Eur Respir J 23(6):885–890

    Article  PubMed  CAS  Google Scholar 

  88. Miyata M et al (2002) Non-linear behaviour of respiratory movement in obstructive sleep apnoea syndrome. Clin Physiol Funct Imaging 22(5):320–327

    Article  PubMed  Google Scholar 

  89. Veiga J et al (2010) Approximate entropy as a measure of the airflow pattern complexity in asthma. Conf Proc IEEE Eng Med Biol Soc 2010:2463–2466

    PubMed  Google Scholar 

  90. Wysocki M et al (2006) Reduced breathing variability as a predictor of unsuccessful patient separation from mechanical ventilation. Crit Care Med 34(8):2076–2083

    Article  PubMed  Google Scholar 

  91. Yeragani VK et al (2002) Nonlinear measures of respiration: respiratory irregularity and increased chaos of respiration in patients with panic disorder. Neuropsychobiology 46(3): 111–120

    Article  PubMed  Google Scholar 

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Acknowledgments

This research has been generously supported by NIH. HL-087377, NS069220 and VA Research Service I01BX000873.

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Correspondence to Thomas E. Dick Ph.D. .

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Dick, T.E. et al. (2013). Analysis of Ventilatory Pattern Variability. In: Vodovotz, Y., An, G. (eds) Complex Systems and Computational Biology Approaches to Acute Inflammation. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8008-2_5

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