title: Estimating respiratory pattern variability by symbolic dynamics.
abstract: OBJECTIVE
  The traditional techniques of data analysis are often not sufficient to characterize the complex dynamics of respiration. In this study the respiratory pattern variability was analyzed using symbolic dynamics.
  
  
  METHODS
  A group of 20 patients on weaning trials from mechanical ventilation were studied at two different pressure support ventilation levels. Breath duration (T(TOT)) time series and the relation T(I)/T(TOT), that contains the influence of inspiratory time (T(I)), were considered. Length-3 words and 3 different symbols were proposed. The incidence of the overlapping tau and the parameter alpha were analyzed.
  
  
  RESULTS
  From the breath duration time series, the distribution of words with probability of occurrence higher than 6% was concentrated on one word for low respiratory variability, whereas high variability was characterized by 4 words, presenting a statistically significant difference (p </= 0.0005). The probability occurrence of words "110" and "111" was also significantly different (p</= 0.0005) when comparing both variabilities.
  
  
  CONCLUSIONS
  The analysis carried out obtained discriminant functions able to correctly classify all the testing set series. These results permit the consideration of symbolic dynamics as a promising methodology to study the respiratory pattern variability.
authors.affiliation: Dep. ESAII, C/ Pau Gargallo, 5, 08028 Barcelona, Spain. caminal@creb.upc.es
authors.forename: P
authors.initials: P
authors.lastname: Caminal
authors.forename: J
authors.initials: J
authors.lastname: Mateu
authors.forename: M
authors.initials: M
authors.lastname: Vallverdú
authors.forename: B
authors.initials: B
authors.lastname: Giraldo
authors.forename: S
authors.initials: S
authors.lastname: Benito
authors.forename: A
authors.initials: A
authors.lastname: Voss
mesh_terms: D003627:Data Interpretation, Statistical; D005541:Forced Expiratory Volume / Q000502:physiology*; D006801:Humans; D015994:Incidence; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D011336:Probability; D012016:Reference Values; D015656:Respiratory Mechanics / Q000502:physiology*; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors; D015300:Ventilator Weaning