Optimized symbolic dynamics approach for the analysis of the respiratory pattern

P. Caminal, M. Vallverdú, B. Giraldo, S. Benito, G. Vázquez, A. Voss

Research output: Contribution to journalArticleResearchpeer-review

22 Citations (Scopus)


Traditional time domain techniques of data analysis are often not sufficient to characterize the complex dynamics of respiration. In this paper, the respiratory pattern variability is analyzed using symbolic dynamics. A group of 20 patients on weaning trials from mechanical ventilation are studied at two different pressure support ventilation levels, in order to obtain respiratory volume signals with different variability. Time series of inspiratory time, expiratory time, breathing duration, fractional inspiratory time, tidal volume and mean inspiratory flow are analyzed. Two different symbol alphabets, with three and four symbols, are considered to characterize the respiratory pattern variability. Assessment of the method is made using the 40 respiratory volume signals classified using clinical criteria into two classes: low variability (LV) or high variability (HV). A discriminant analysis using single indexes from symbolic dynamics has been able to classify the respiratory volume signals with an out-of-sample accuracy of 100%. © 2005 IEEE.
Original languageEnglish
Pages (from-to)1832-1839
JournalIEEE Transactions on Biomedical Engineering
Issue number11
Publication statusPublished - 1 Nov 2005


  • Data classification
  • Dynamical nonlinearities analysis
  • Respiratory pattern variability
  • Symbolic dynamics


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