M. Small et al., Is breathing in infants chaotic? Dimension estimates for respiratory patterns during quiet sleep, J APP PHYSL, 86(1), 1999, pp. 359-376
We describe an analysis of dynamic behavior apparent in times-series record
ings of infant breathing during sleep. Three principal techniques were used
: estimation of correlation dimension, surrogate data analysis, and reduced
linear (autoregressive) modeling (RARM). Correlation dimension can be used
to quantify the complexity of time series and has been applied to a variet
y of physiological and biological measurements. However, the methods most c
ommonly used to estimate correlation dimension suffer from some technical p
roblems that can produce misleading results if not correctly applied. We us
ed a new technique of estimating correlation dimension that has fewer probl
ems. We tested the significance of dimension estimates by comparing estimat
es with artificial data sets (surrogate data). On the basis of the analysis
, we conclude that the dynamics of infant breathing during quiet sleep can
best be described as a nonlinear dynamic system with large-scale, low-dimen
sional and small-scale, high-dimensional behavior; more specifically, a noi
se-driven nonlinear system with a two-dimensional periodic orbit. Using our
RARM technique, we identified the second period as cyclic amplitude modula
tion of the same period as periodic breathing. We conclude that our data ar
e consistent with respiration being chaotic.