Respiratory pattern variability analysis based on nonlinear dynamics methods

The traditional techniques of data analysis are often not sufficient to characterize the complex dynamics of respiration. In this study the respiratory pattern complexity at different levels of pressure support ventilation (PSV) are analyzed using symbolic dynamics. A group of 18 patients on weaning trials from mechanical ventilation have been studied at two different PSV levels. Inspiratory time (Ti) and breath duration (Ttot) time series were considered. Length-3 words and 4 different symbols were proposed. The incidence of the data length n, the overlapping z and the parameter a were analyzed. From the breath duration time series, the distribution of words with probability of occurrence higher than 1% is concentrated on 14 words for low pressure of PSV, whereas high pressure is characterized by a more uniform distribution, presenting a statistically significant difference (p<0.004). The probability of occurrence of words “012”, “233” and “323” is significantly different (p<0.001) when the patients are submitted at different PSV levels. Therefore, it may be concluded that the respiratory pattern variability depends on the pressure support ventilation